Academic literature on the topic 'Generative Adversarial Neural Networks (GAN's)'
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Journal articles on the topic "Generative Adversarial Neural Networks (GAN's)"
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Wang, Lei, Liang Zeng, and Jian Li. "AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-Series Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10140–48. http://dx.doi.org/10.1609/aaai.v37i8.26208.
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Large-scale high-quality data is critical for training modern deep neural networks. However, data acquisition can be costly or time-consuming for many time-series applications, thus researchers turn to generative models for generating synthetic time-series data. In particular, recent generative adversarial networks (GANs) have achieved remarkable success in time-series generation. Despite their success, existing GAN models typically generate the sequences in an auto-regressive manner, and we empirically observe that they suffer from severe distribution shifts and bias amplification, especially when generating long sequences. To resolve this problem, we propose Adversarial Error Correction GAN (AEC-GAN), which is capable of dynamically correcting the bias in the past generated data to alleviate the risk of distribution shifts and thus can generate high-quality long sequences. AEC-GAN contains two main innovations: (1) We develop an error correction module to mitigate the bias. In the training phase, we adversarially perturb the realistic time-series data and then optimize this module to reconstruct the original data. In the generation phase, this module can act as an efficient regulator to detect and mitigate the bias. (2) We propose an augmentation method to facilitate GAN's training by introducing adversarial examples. Thus, AEC-GAN can generate high-quality sequences of arbitrary lengths, and the synthetic data can be readily applied to downstream tasks to boost their performance. We conduct extensive experiments on six widely used datasets and three state-of-the-art time-series forecasting models to evaluate the quality of our synthetic time-series data in different lengths and downstream tasks. Both the qualitative and quantitative experimental results demonstrate the superior performance of AEC-GAN over other deep generative models for time-series generation.
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Shah, Chirag Vinalbhai. "Exploring Generative Adversarial Networks: Applications of GANs in AI and Big Data." Global Research and Development Journals 7, no. 5 (2022): 11–20. http://dx.doi.org/10.70179/grdjev09i100011.
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This chapter explores generative adversarial networks (GANs), which simultaneously train two models: a generator (G) to generate new instances that resemble a training data set; and a discriminator (D) that estimates the probability that an instance was produced by the generator rather than belonging to the training data. The capabilities of GANs are explored, thrusting GANs into the forefront of AI research and development. Preparation of the data input to the generative adversarial network occurs before the application of the discriminator to the multilayer perceptron neural network designed to solve an example classification problem. Then, utilizing GANs to facilitate big data implementation is suggested, simultaneously imputing the missing data and selecting a smaller more effective training set for an example classification problem. AI problems can be defined as realizations of various data models using a multilayer perceptron neural network, which are here illustrated and employed to solve the "and" (AND) function approximation, a famous AI hard problem. Also defined and described. Note that multi-GANs are employed to select an overall effective representative training data set for classification problems, facilitate big data, and predict missing data values during the preparation of the training set. This work differs from the recent efforts attempting to improve GANs. A hyperparameter tuning methodology. Furthermore, this paper couples the imputation of missing no-label binary data with data in the training set used as an input to a multilayer perceptron neural network, which has typically shown an improved estimate of the models needed for binary and optimization results when samples are selected to represent the "and" (AND) function classification problem training process for mid- and large-scale problems.
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Ambu, Karthik, Shetty Jyoti, G. Shobha, and Dev Roger. "Implementation of generative adversarial networks in HPCC systems using GNN bundle." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 374–81. https://doi.org/10.11591/ijai.v10.i2.pp374-381.
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HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In order to make sure that they train as adversaries, a certain weights transfer methodology was implemented. MNIST dataset which has been used to test the proposed approach has provided satisfactory results. The results obtained were unique images very similar to the MNIST dataset, as it were expected.
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Purwono, Purwono, Annastasya Nabila Elsa Wulandari, Alfian Ma'arif, and Wael A. Salah. "Understanding Generative Adversarial Networks (GANs): A Review." Control Systems and Optimization Letters 3, no. 1 (2025): 36–45. https://doi.org/10.59247/csol.v3i1.170.
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Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data. This dynamic interaction forms a minimax game that produces high-quality synthetic data. Since its introduction in 2014 by Ian Goodfellow, GAN has evolved through various innovative architectures, including Vanilla GAN, Conditional GAN (cGAN), Deep Convolutional GAN (DCGAN), CycleGAN, StyleGAN, Wasserstein GAN (WGAN), and BigGAN. Each of these architectures presents a novel approach to address technical challenges such as training stability, data diversification, and result quality. GANs have been widely applied in various sectors. In healthcare, GANs are used to generate synthetic medical images that support diagnostic development without violating patient privacy. In the media and entertainment industry, GANs facilitate the enhancement of image and video resolution, as well as the creation of realistic content. However, the development of GANs faces challenges such as mode collapse, training instability, and inadequate quality evaluation. In addition to technical challenges, GANs raise ethical issues, such as the misuse of the technology for deepfake creation. Legal regulations, detection tools, and public education are important mitigation measures. Future trends suggest that GANs will be increasingly used in text-to-image synthesis, realistic video generation, and integration with multimodal systems to support cross-disciplinary innovation.
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Aynaan, Quraishi, Jethwa Jaydeep, and Gupta Shiwani. "Synthetic audio and video generation for language translation using GANs." i-manager's Journal on Augmented & Virtual Reality 1, no. 1 (2023): 1. http://dx.doi.org/10.26634/javr.1.1.19412.
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Language barriers create a digital divide that prevents people from benefiting from the vast amount of content produced worldwide. In addition, content creators face challenges in producing content in multiple languages to reach a wider audience. To address this problem, this study proposed a solution through a survey that utilized Generative Adversarial Networks (GAN), Natural Language Processing (NPL), and Computer Vision. A Generative Adversarial Network (GAN) is a Machine Learning (ML) model in which two neural networks compete with each other by using deep learning methods to obtain more accurate predictions. The solution provided in this study can generate synthesized videos that are close to reality, ultimately bridging the language barrier and providing access to content.
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Alanazi, Meshari Huwaytim. "G-GANS for Adaptive Learning in Dynamic Network Slices." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14327–41. http://dx.doi.org/10.48084/etasr.7046.
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This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.
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Zhang, Youliang, Guowu Yuan, Hao Wu, and Hao Zhou. "MAE-GAN: a self-supervised learning-based classification model for cigarette appearance defects." Applied Computing and Intelligence 4, no. 2 (2024): 253–68. https://doi.org/10.3934/aci.2024015.
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<p>Appearance defects frequently occur during cigarette manufacturing due to production equipment or raw materials. Appearance defects significantly impact the quality of tobacco products. Since manual inspection cannot keep pace with the demands of high-speed production lines, rapid and accurate automated classification and detection are essential. Supervised learning is predominantly employed in research on automated classification of product quality appearance defects. However, supervised learning necessitates substantial labeled data for training, which is time-consuming to annotate and prone to errors. This paper proposes a self-supervised learning-based classification model for cigarette appearance defects. This is a generative adversarial network (GAN) model based on masked autoencoders (MAE), called MAE-GAN. First, this model combines MAE as a generator with a simple discriminator to form a generative adversarial network according to the principle of mask reconstruction in MAE. The generator reconstructs the images to learn their features. Second, the model also integrates MAE's loss function into the GAN's loss function. This lets the model focus on pixel-level losses during training. As a result, model performance is improved. Third, a Wasserstein GAN with gradient penalty (WGAN-GP) is added to stabilize the training process. In addition, this paper preprocesses cigarette images through segmentation and recombination. Neural networks typically accept images with the same width and height. Due to the narrow shape of cigarette images, if the image is directly transformed into a square and fed into a neural network, the details of the image will be severely lost. This paper segments the cigarette image into three parts and recombines them into images with similar length and width, greatly improving classification accuracy.</p>
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El-Shal, Ibrahim H., Omar M. Fahmy, and Mustafa A. Elattar. "License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)." IEEE Access 10 (2022): 30846–57. http://dx.doi.org/10.1109/access.2022.3157714.
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Vyshnavi, Tiramdasu, and Jatinder Kaur. "Decipherment of script - sing synthetic data generator’s." Journal of Information and Optimization Sciences 46, no. 3 (2025): 783–91. https://doi.org/10.47974/jios-1797.
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This paper is created to give a brief idea about the generative adversarial networks and the handwritten digits recognition using the synthetic data generators with the MNIST data. In this paper Tensorflow is used along with GAN’s to recognise the handwritten digits. The 2014 introduction of Generative Adversarial Networks (GANs) by Lan J. Goodfellow revolutionized deep learning and artificial intelligence. A subclass of generative models known as GANs uses a dual neural network structure—a generator and a discriminator— to transform unsupervised learning. GANs create varied, real data samples, influencing the design, entertainment, and healthcare sectors. They are employed in a variety of fields, notably text-to-image translation and image processing. Deep learning relies heavily on neural networks, which are also used in pattern analysis and picture identification. The generative models that are discussed here produce data that is similar to the source datasets and is categorized using density estimation. One popular kind of GANs is made up of a discriminator that separates created data from real data and a generator that produces synthetic data[5][[9]. Their adversarial training improves the discriminator’s ability to distinguish between actual and false samples while honing the generator’s capacity to generate realistic data. Neural network activation factors introduce non-linearity; their applicability varies throughout models and tasks. An open-source package called TensorFlow makes GANs and other AI tasks easier[3][15]. Using the MNIST dataset, an experiment demonstrating handwritten digit recognition using TensorFlow and the Rectified Linear Unit (ReLu) activation function is presented. After several epochs, the results show a significant improvement, demonstrating the effectiveness of GANs in picture identification.
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Park, Sung-Wook, Jun-Ho Huh, and Jong-Chan Kim. "BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference." Electronics 9, no. 4 (2020): 688. http://dx.doi.org/10.3390/electronics9040688.
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In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS.
Dissertations / Theses on the topic "Generative Adversarial Neural Networks (GAN's)"
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Delacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.
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The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. These kind of algorithms need the input images used for the training to be labeled, and even if there is a large amount of images most of them are not labeled and it takes structural engineers a large amount of time to do it. The current data earthquakes image data bases do not contain the label information or is incomplete slowing significantly the advance of a solution and are incredible difficult to search. To be able to train a ML algorithm to classify one of the structural damages it took the architecture school an entire year to gather 200 images of the specific damage. That number is clearly not enough to avoid overfitting so for this thesis we decided to generate synthetic images for the specific structural damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to better prepare the reconnaissance teams that manage recovery tasks. GANs combine classification neural networks with generative neural networks. For this thesis we will be combining a convolutional neural network (CNN) with a generative neural network. By taking a classifier trained in a GAN and modifying it to classify other images the classifier can take advantage of the GAN training without having to find more training data. The classifier trained in this way was able to achieve an 88\% accuracy score when classifying images of structural damage caused by earthquakes.
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Kryściński, Wojciech. "Training Neural Models for Abstractive Text Summarization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236973.
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Abstractive text summarization aims to condense long textual documents into a short, human-readable form while preserving the most important information from the source document. A common approach to training summarization models is by using maximum likelihood estimation with the teacher forcing strategy. Despite its popularity, this method has been shown to yield models with suboptimal performance at inference time. This work examines how using alternative, task-specific training signals affects the performance of summarization models. Two novel training signals are proposed and evaluated as part of this work. One, a novelty metric, measuring the overlap between n-grams in the summary and the summarized article. The other, utilizing a discriminator model to distinguish human-written summaries from generated ones on a word-level basis. Empirical results show that using the mentioned metrics as rewards for policy gradient training yields significant performance gains measured by ROUGE scores, novelty scores and human evaluation.<br>Abstraktiv textsammanfattning syftar på att korta ner långa textdokument till en förkortad, mänskligt läsbar form, samtidigt som den viktigaste informationen i källdokumentet bevaras. Ett vanligt tillvägagångssätt för att träna sammanfattningsmodeller är att använda maximum likelihood-estimering med teacher-forcing-strategin. Trots dess popularitet har denna metod visat sig ge modeller med suboptimal prestanda vid inferens. I det här arbetet undersöks hur användningen av alternativa, uppgiftsspecifika träningssignaler påverkar sammanfattningsmodellens prestanda. Två nya träningssignaler föreslås och utvärderas som en del av detta arbete. Den första, vilket är en ny metrik, mäter överlappningen mellan n-gram i sammanfattningen och den sammanfattade artikeln. Den andra använder en diskrimineringsmodell för att skilja mänskliga skriftliga sammanfattningar från genererade på ordnivå. Empiriska resultat visar att användandet av de nämnda mätvärdena som belöningar för policygradient-träning ger betydande prestationsvinster mätt med ROUGE-score, novelty score och mänsklig utvärdering.
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Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.
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Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this purpose. The unique model was shown both qualitatively and quantitatively to generate the best data sets. A set of smaller experiments on standard data sets also revealed cases where this generative model could improve the performance of an existing discriminative model. The results indicate that generative models can be used to augment or replace existing data sets when training discriminative models.<br>Dataaugmentering är en teknik som kan utföras på flera sätt för att förbättra träningen av diskriminativa modeller. De senaste framgångarna inom djupa generativa modeller har öppnat upp nya sätt att augmentera existerande dataset. I detta arbete har ett ramverk för augmentering av annoterade dataset med hjälp av djupa generativa modeller föreslagits. Utöver detta så har en metod för kvantitativ evaulering av kvaliteten hos genererade data set tagits fram. Med hjälp av detta ramverk har två dataset för pupillokalisering genererats med olika generativa modeller. Både väletablerade modeller och en ny modell utvecklad för detta syfte har testats. Den unika modellen visades både kvalitativt och kvantitativt att den genererade de bästa dataseten. Ett antal mindre experiment på standardiserade dataset visade exempel på fall där denna generativa modell kunde förbättra prestandan hos en existerande diskriminativ modell. Resultaten indikerar att generativa modeller kan användas för att augmentera eller ersätta existerande dataset vid träning av diskriminativa modeller.
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Aftab, Nadeem. "Disocclusion Inpainting using Generative Adversarial Networks." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40502.
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The old methods used for images inpainting of the Depth Image Based Rendering (DIBR) process are inefficient in producing high-quality virtual views from captured data. From the viewpoint of the original image, the generated data’s structure seems less distorted in the virtual view obtained by translation but when then the virtual view involves rotation, gaps and missing spaces become visible in the DIBR generated data. The typical approaches for filling the disocclusion tend to be slow, inefficient, and inaccurate. In this project, a modern technique Generative Adversarial Network (GAN) is used to fill the disocclusion. GAN consists of two or more neural networks that compete against each other and get trained. This study result shows that GAN can inpaint the disocclusion with a consistency of the structure. Additionally, another method (Filling) is used to enhance the quality of GAN and DIBR images. The statistical evaluation of results shows that GAN and filling method enhance the quality of DIBR images.
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Käll, Viktor, and Erik Piscator. "Particle Filter Bridge Interpolation in GANs." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301733.
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Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. This similarity measure gives rise to the possibility of interpolating in the data which has been done successfully in the past. Herein we propose a new stochastic interpolation method for GANs where the interpolation is forced to adhere to the data distribution by implementing a sequential Monte Carlo algorithm for data sampling. The results show that the new method outperforms previously known interpolation methods for the data set LINES; compared to the results of other interpolation methods there was a significant improvement measured through quantitative and qualitative evaluations. The developed interpolation method has met its expectations and shown promise, however it needs to be tested on a more complex data set in order to verify that it also scales well.<br>Generative adversarial networks (GANs) är ett slags generativ modell som har fått mycket uppmärksamhet de senaste åren sedan de upptäcktes för sin potential att återskapa komplexa högdimensionella datafördelningar. Dessa förser en komprimerad representation av datan där enbart de karaktäriserande egenskaperna är bevarade, vilket följdaktligen inducerar ett avståndsmått på datarummet. Detta avståndsmått möjliggör interpolering inom datan vilket har åstadkommits med framgång tidigare. Häri föreslår vi en ny stokastisk interpoleringsmetod för GANs där interpolationen tvingas följa datafördelningen genom att implementera en sekventiell Monte Carlo algoritm för dragning av datapunkter. Resultaten för studien visar att metoden ger bättre interpolationer för datamängden LINES som användes; jämfört med resultaten av tidigare kända interpolationsmetoder syntes en märkbar förbättring genom kvalitativa och kvantitativa utvärderingar. Den framtagna interpolationsmetoden har alltså mött förväntningarna och är lovande, emellertid fordras att den testas på en mer komplex datamängd för att bekräfta att den fungerar väl även under mer generella förhållanden.
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Daley, Jr John. "Generating Synthetic Schematics with Generative Adversarial Networks." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20901.
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This study investigates synthetic schematic generation using conditional generative adversarial networks, specifically the Pix2Pix algorithm was implemented for the experimental phase of the study. With the increase in deep neural network’s capabilities and availability, there is a demand for verbose datasets. This in combination with increased privacy concerns, has led to synthetic data generation utilization. Analysis of synthetic images was completed using a survey. Blueprint images were generated and were successful in passing as genuine images with an accuracy of 40%. This study confirms the ability of generative neural networks ability to produce synthetic blueprint images.
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Yamazaki, Hiroyuki Vincent. "On Depth and Complexity of Generative Adversarial Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217293.
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Although generative adversarial networks (GANs) have achieved state-of-the-art results in generating realistic look- ing images, they are often parameterized by neural net- works with relatively few learnable weights compared to those that are used for discriminative tasks. We argue that this is suboptimal in a generative setting where data is of- ten entangled in high dimensional space and models are ex- pected to benefit from high expressive power. Additionally, in a generative setting, a model often needs to extrapo- late missing information from low dimensional latent space when generating data samples while in a typical discrimina- tive task, the model only needs to extract lower dimensional features from high dimensional space. We evaluate different architectures for GANs with varying model capacities using shortcut connections in order to study the impacts of the capacity on training stability and sample quality. We show that while training tends to oscillate and not benefit from additional capacity of naively stacked layers, GANs are ca- pable of generating samples with higher quality, specifically for images, samples of higher visual fidelity given proper regularization and careful balancing.<br>Trots att Generative Adversarial Networks (GAN) har lyckats generera realistiska bilder består de än idag av neurala nätverk som är parametriserade med relativt få tränbara vikter jämfört med neurala nätverk som används för klassificering. Vi tror att en sådan modell är suboptimal vad gäller generering av högdimensionell och komplicerad data och anser att modeller med högre kapaciteter bör ge bättre estimeringar. Dessutom, i en generativ uppgift så förväntas en modell kunna extrapolera information från lägre till högre dimensioner medan i en klassificeringsuppgift så behöver modellen endast att extrahera lågdimensionell information från högdimensionell data. Vi evaluerar ett flertal GAN med varierande kapaciteter genom att använda shortcut connections för att studera hur kapaciteten påverkar träningsstabiliteten, samt kvaliteten av de genererade datapunkterna. Resultaten visar att träningen blir mindre stabil för modeller som fått högre kapaciteter genom naivt tillsatta lager men visar samtidigt att datapunkternas kvaliteter kan öka, specifikt för bilder, bilder med hög visuell fidelitet. Detta åstadkoms med hjälp utav regularisering och noggrann balansering.
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Castillo, Araújo Victor. "Ensembles of Single Image Super-Resolution Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290945.
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Generative Adversarial Networks have been used to obtain state-of-the-art results for low-level computer vision tasks like single image super-resolution, however, they are notoriously difficult to train due to the instability related to the competing minimax framework. Additionally, traditional ensembling mechanisms cannot be effectively applied with these types of networks due to the resources they require at inference time and the complexity of their architectures. In this thesis an alternative method to create ensembles of individual, more stable and easier to train, models by using interpolations in the parameter space of the models is found to produce better results than those of the initial individual models when evaluated using perceptual metrics as a proxy of human judges. This method can be used as a framework to train GANs with competitive perceptual results in comparison to state-of-the-art alternatives.<br>Generative Adversarial Networks (GANs) har använts för att uppnå state-of-the- art resultat för grundläggande bildanalys uppgifter, som generering av högupplösta bilder från bilder med låg upplösning, men de är notoriskt svåra att träna på grund av instabiliteten relaterad till det konkurrerande minimax-ramverket. Dessutom kan traditionella mekanismer för att generera ensembler inte tillämpas effektivt med dessa typer av nätverk på grund av de resurser de behöver vid inferenstid och deras arkitekturs komplexitet. I det här projektet har en alternativ metod för att samla enskilda, mer stabila och modeller som är lättare att träna genom interpolation i parameterrymden visat sig ge bättre perceptuella resultat än de ursprungliga enskilda modellerna och denna metod kan användas som ett ramverk för att träna GAN med konkurrenskraftig perceptuell prestanda jämfört med toppmodern teknik.
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Garcia, Torres Douglas. "Generation of Synthetic Data with Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254366.
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The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when the data is sensitive to use, or simply when it is hard to get access to the real data. Traditional methods of synthetic data generation use techniques that do not intend to replicate important statistical properties of the original data. Properties such as the distribution, the patterns or the correlation between variables, are often omitted. Moreover, most of the existing tools and approaches require a great deal of user-defined rules and do not make use of advanced techniques like Machine Learning or Deep Learning. While Machine Learning is an innovative area of Artificial Intelligence and Computer Science that uses statistical techniques to give computers the ability to learn from data, Deep Learning is a closely related field based on learning data representations, which may serve useful for the task of synthetic data generation. This thesis focuses on one of the most interesting and promising innovations of the last years in the Machine Learning community: Generative Adversarial Networks. An approach for generating discrete, continuous or text synthetic data with Generative Adversarial Networks is proposed, tested, evaluated and compared with a baseline approach. The results prove the feasibility and show the advantages and disadvantages of using this framework. Despite its high demand for computational resources, a Generative Adversarial Networks framework is capable of generating quality synthetic data that preserves the statistical properties of a given dataset.<br>Syftet med syntetisk datagenerering är att tillhandahålla data som inte är verkliga i fall där användningen av reella data på något sätt är begränsad. Till exempel, när det finns behov av större datamängder, när data är känsliga för användning, eller helt enkelt när det är svårt att få tillgång till den verkliga data. Traditionella metoder för syntetiska datagenererande använder tekniker som inte avser att replikera viktiga statistiska egenskaper hos de ursprungliga data. Egenskaper som fördelningen, mönstren eller korrelationen mellan variabler utelämnas ofta. Dessutom kräver de flesta av de befintliga verktygen och metoderna en hel del användardefinierade regler och använder inte avancerade tekniker som Machine Learning eller Deep Learning. Machine Learning är ett innovativt område för artificiell intelligens och datavetenskap som använder statistiska tekniker för att ge datorer möjlighet att lära av data. Deep Learning ett närbesläktat fält baserat på inlärningsdatapresentationer, vilket kan vara användbart för att generera syntetisk data. Denna avhandling fokuserar på en av de mest intressanta och lovande innovationerna från de senaste åren i Machine Learning-samhället: Generative Adversarial Networks. Generative Adversarial Networks är ett tillvägagångssätt för att generera diskret, kontinuerlig eller textsyntetisk data som föreslås, testas, utvärderas och jämförs med en baslinjemetod. Resultaten visar genomförbarheten och visar fördelarna och nackdelarna med att använda denna metod. Trots dess stora efterfrågan på beräkningsresurser kan ett generativt adversarialnätverk skapa generell syntetisk data som bevarar de statistiska egenskaperna hos ett visst dataset.
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Nistal, Hurlé Javier. "Exploring generative adversarial networks for controllable musical audio synthesis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT009.
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Les synthétiseurs audio sont des instruments de musique électroniques qui génèrent des sons artificiels sous un certain contrôle paramétrique. Alors que les synthétiseurs ont évolué depuis leur popularisation dans les années 70, deux défis fondamentaux restent encore non résolus: 1) le développement de systèmes de synthèse répondant à des paramètres sémantiquement intuitifs; 2) la conception de techniques de synthèse «universelles», indépendantes de la source à modéliser. Cette thèse étudie l’utilisation des réseaux adversariaux génératifs (ou GAN) pour construire de tels systèmes. L’objectif principal est de rechercher et de développer de nouveaux outils pour la production musicale, qui offrent des moyens intuitifs de manipulation du son, par exemple en contrôlant des paramètres qui répondent aux propriétés perceptives du son et à d’autres caractéristiques. Notre premier travail étudie les performances des GAN lorsqu’ils sont entraînés sur diverses représentations de signaux audio. Ces expériences comparent différentes formes de données audio dans le contexte de la synthèse sonore tonale. Les résultats montrent que la représentation magnitude-fréquence instantanée et la transformée de Fourier à valeur complexe obtiennent les meilleurs résultats. En s’appuyant sur ce résultat, notre travail suivant présente DrumGAN, un synthétiseur audio de sons percussifs. En conditionnant le modèle sur des caractéristiques perceptives décrivant des propriétés timbrales de haut niveau, nous démontrons qu’un contrôle intuitif peut être obtenu sur le processus de génération. Ce travail aboutit au développement d’un plugin VST générant de l’audio haute résolution. La rareté des annotations dans les ensembles de données audio musicales remet en cause l’application de méthodes supervisées pour la génération conditionnelle. On utilise une approche de distillation des connaissances pour extraire de telles annotations à partir d’un système d’étiquetage audio préentraîné. DarkGAN est un synthétiseur de sons tonaux qui utilise les probabilités de sortie d’un tel système (appelées « étiquettes souples ») comme informations conditionnelles. Les résultats montrent que DarkGAN peut répondre modérément à de nombreux attributs intuitifs, même avec un conditionnement d’entrée hors distribution. Les applications des GAN à la synthèse audio apprennent généralement à partir de données de spectrogramme de taille fixe. Nous abordons cette limitation en exploitant une méthode auto-supervisée pour l’apprentissage de caractéristiques discrètes à partir de données séquentielles. De telles caractéristiques sont utilisées comme entrée conditionnelle pour fournir au modèle des informations dépendant du temps par étapes. La cohérence globale est assurée en fixant le bruit d’entrée z (caractéristique en GANs). Les résultats montrent que, tandis que les modèles entraînés sur un schéma de taille fixe obtiennent une meilleure qualité et diversité audio, les nôtres peuvent générer avec compétence un son de n’importe quelle durée. Une direction de recherche intéressante est la génération d’audio conditionnée par du matériel musical préexistant. Nous étudions si un générateur GAN, conditionné sur des signaux audio musicaux hautement compressés, peut générer des sorties ressemblant à l’audio non compressé d’origine. Les résultats montrent que le GAN peut améliorer la qualité des signaux audio par rapport aux versions MP3 pour des taux de compression très élevés (16 et 32 kbit/s). En conséquence directe de l’application de techniques d’intelligence artificielle dans des contextes musicaux, nous nous demandons comment la technologie basée sur l’IA peut favoriser l’innovation dans la pratique musicale. Par conséquent, nous concluons cette thèse en offrant une large perspective sur le développement d’outils d’IA pour la production musicale, éclairée par des considérations théoriques et des rapports d’utilisation d’outils d’IA dans le monde réel par des artistes professionnels<br>Audio synthesizers are electronic musical instruments that generate artificial sounds under some parametric control. While synthesizers have evolved since they were popularized in the 70s, two fundamental challenges are still unresolved: 1) the development of synthesis systems responding to semantically intuitive parameters; 2) the design of "universal," source-agnostic synthesis techniques. This thesis researches the use of Generative Adversarial Networks (GAN) towards building such systems. The main goal is to research and develop novel tools for music production that afford intuitive and expressive means of sound manipulation, e.g., by controlling parameters that respond to perceptual properties of the sound and other high-level features. Our first work studies the performance of GANs when trained on various common audio signal representations (e.g., waveform, time-frequency representations). These experiments compare different forms of audio data in the context of tonal sound synthesis. Results show that the Magnitude and Instantaneous Frequency of the phase and the complex-valued Short-Time Fourier Transform achieve the best results. Building on this, our following work presents DrumGAN, a controllable adversarial audio synthesizer of percussive sounds. By conditioning the model on perceptual features describing high-level timbre properties, we demonstrate that intuitive control can be gained over the generation process. This work results in the development of a VST plugin generating full-resolution audio and compatible with any Digital Audio Workstation (DAW). We show extensive musical material produced by professional artists from Sony ATV using DrumGAN. The scarcity of annotations in musical audio datasets challenges the application of supervised methods to conditional generation settings. Our third contribution employs a knowledge distillation approach to extract such annotations from a pre-trained audio tagging system. DarkGAN is an adversarial synthesizer of tonal sounds that employs the output probabilities of such a system (so-called “soft labels”) as conditional information. Results show that DarkGAN can respond moderately to many intuitive attributes, even with out-of-distribution input conditioning. Applications of GANs to audio synthesis typically learn from fixed-size two-dimensional spectrogram data analogously to the "image data" in computer vision; thus, they cannot generate sounds with variable duration. In our fourth paper, we address this limitation by exploiting a self-supervised method for learning discrete features from sequential data. Such features are used as conditional input to provide step-wise time-dependent information to the model. Global consistency is ensured by fixing the input noise z (characteristic in adversarial settings). Results show that, while models trained on a fixed-size scheme obtain better audio quality and diversity, ours can competently generate audio of any duration. One interesting direction for research is the generation of audio conditioned on preexisting musical material, e.g., the generation of some drum pattern given the recording of a bass line. Our fifth paper explores a simple pretext task tailored at learning such types of complex musical relationships. Concretely, we study whether a GAN generator, conditioned on highly compressed MP3 musical audio signals, can generate outputs resembling the original uncompressed audio. Results show that the GAN can improve the quality of the audio signals over the MP3 versions for very high compression rates (16 and 32 kbit/s). As a direct consequence of applying artificial intelligence techniques in musical contexts, we ask how AI-based technology can foster innovation in musical practice. Therefore, we conclude this thesis by providing a broad perspective on the development of AI tools for music production, informed by theoretical considerations and reports from real-world AI tool usage by professional artists
Books on the topic "Generative Adversarial Neural Networks (GAN's)"
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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.
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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ
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Ahirwar, Kailash. Generative Adversarial Networks Projects: Build Next-Generation Generative Models Using TensorFlow and Keras. Packt Publishing, Limited, 2019.
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Kalin, Josh. Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras. Packt Publishing, 2018.
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Walters, Greg, and John Hany. Hands-On Generative Adversarial Networks with Pytorch 1. x: Implement Next-Generation Neural Networks to Build Powerful GAN Models Using Python. Packt Publishing, Limited, 2019.
Find full textBook chapters on the topic "Generative Adversarial Neural Networks (GAN's)"
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Stamm, Matthew C., and Xinwei Zhao. "Anti-Forensic Attacks Using Generative Adversarial Networks." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_17.
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AbstractThe rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat posed by GAN-based anti-forensic attacks. GANs are a powerful machine learning framework that can be used to create realistic, but completely synthetic data. Researchers have recently shown that anti-forensic attacks can be built by using GANs to create synthetic forensic traces. While only a small number of GAN-based anti-forensic attacks currently exist, results show these early attacks are both effective at fooling forensic algorithms and introduce very little distortion into attacked images.
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Anil Kumar, Kakelli, Binamra Neupane, Saugat Malla, and Durga Prasad Pandey. "COVID-19 Disease Prediction Using Generative Adversarial Networks with Convolutional Neural Network (GANs-CNN) Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53085-2_12.
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Anwar, Suzan, Mardin Anwer, and Daniah Al-Nadawi. "DeepFake Technology for Breast Cancer Dataset Generation Using Autoencoders and Deep Neural Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_1.
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Abstract In the emerging field of radiogenomics, the primary challenge is the high cost of genetic testing, which restricts access to large, paired datasets of imaging and genetic information. Such datasets are essential for the effective training of machine learning algorithms in radiogenomic analyses. This research aims to bridge the gap between gene expression in tumors and their morphological representation in MRI scans of breast cancer patients. In this work an advanced autoencoder for processing gene expression data, and the derived weights from this autoencoder utilized were then employed to initialize a supervised Deep Neural Network (DNN). This network extracted distinct morphological markers from each MRI scan . This study introduces an innovative approach that utilizes deepfake technology, employing dual Generative Adversarial Networks (GANs) to generate synthetic imaging data from a radiogenomic dataset. This synthetic data, nearly indistinguishable from real data, is produced using a supervised neural network and is aimed at enhancing breast cancer diagnostics. Notably, the proposed neural network, when enhanced with an autoencoder and dropout techniques, demonstrated superior predictive accuracy over linear regression models. Specifically, it reduced errors by an average of 1.8% in mean absolute percent error. These findings underscore that the images generated by the proposed model are virtually indistinguishable from authentic images and exhibit high reliability in applications through the PyTorch framework. The results of this study underscore the potential of the proposed methodology to significantly contribute to advancements in breast cancer diagnostics.
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Li, Yuqian, and Weiguo Xu. "Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling." In Proceedings of the 2021 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_3.
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AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.
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Yalçın, Orhan Gazi. "Generative Adversarial Network." In Applied Neural Networks with TensorFlow 2. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6513-0_12.
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Ghayoumi, Mehdi. "Artificial Neural Networks Fundamentals and Architectures." In Generative Adversarial Networks in Practice. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-5.
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Ghayoumi, Mehdi. "Deep Neural Networks (DNNs) Fundamentals and Architectures." In Generative Adversarial Networks in Practice. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-6.
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Kim, Jin-Young, Seok-Jun Bu, and Sung-Bae Cho. "Malware Detection Using Deep Transferred Generative Adversarial Networks." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_58.
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Wei, Miao, and Carl Vogel. "Generative Adversarial Networks in Federated Learning." In Applications of Artificial Intelligence and Neural Systems to Data Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3592-5_32.
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Wang, Zihan, Neng Gao, Xin Wang, Xuexin Qu, and Linghui Li. "SSteGAN: Self-learning Steganography Based on Generative Adversarial Networks." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04179-3_22.
Full textConference papers on the topic "Generative Adversarial Neural Networks (GAN's)"
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Khan, Salaar, Syed Meharullah, and Ali Murtaza. "Improved Neural Network Compression using Generative Adversarial Networks." In 2024 Horizons of Information Technology and Engineering (HITE). IEEE, 2024. https://doi.org/10.1109/hite63532.2024.10777160.
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Shen, Chenjie, Jie Zhu, Lei Yu, Li Yang, and Chun Zuo. "Dependency-Aware Method Naming Framework with Generative Adversarial Sampling." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651109.
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Su, Xingzhe, Daixi Jia, Fengge Wu, Junsuo Zhao, and Changwen Zheng. "Object-aided Generative Adversarial Networks for Remote Sensing Image Generation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650371.
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Li, Feng, Liwen Shi, and Kelong Zhang. "DisGAN: Distance-Aware Generative Adversarial Network for Diverse Image Generation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650204.
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Fernandez, Brandon, Hafiz Yasir Noor, Isaac Woungang, and Glaucio H. S. Carvalho. "Generative Adversarial Networks for IoT Security: A Convolutional Neural Network Approach." In 2024 IEEE Virtual Conference on Communications (VCC). IEEE, 2024. https://doi.org/10.1109/vcc63113.2024.10914482.
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Melendez-Rios, Alexander, Roberto Vega-Berrocal, and Willy Ugarte. "Generative Adversarial Neural Networks for Random and Complex Chord Progression Generation." In 2025 37th Conference of Open Innovations Association (FRUCT). IEEE, 2025. https://doi.org/10.23919/fruct65909.2025.11008228.
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Ni, Yao, Dandan Song, Xi Zhang, Hao Wu, and Lejian Liao. "CAGAN: Consistent Adversarial Training Enhanced GANs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/359.
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Generative adversarial networks (GANs) have shown impressive results, however, the generator and the discriminator are optimized in finite parameter space which means their performance still need to be improved. In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout. As discrepancy between outputs of different sub-networks of a same sample can measure the consistency of these critics, we encourage the critics to be consistent to real samples and inconsistent to generated samples during training, while the generator is trained to generate consistent samples for different critics. Experimental results demonstrate that our method can obtain state-of-the-art Inception scores of 9.17 and 10.02 on supervised CIFAR-10 and unsupervised STL-10 image generation tasks, respectively, as well as achieve competitive semi-supervised classification results on several benchmarks. Importantly, we demonstrate that our method can maintain stability in training and alleviate mode collapse.
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Xiao, Chaowei, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu, and Dawn Song. "Generating Adversarial Examples with Adversarial Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/543.
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Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.
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Chokwitthaya, Chanachok, Edward Collier, Yimin Zhu, and Supratik Mukhopadhyay. "Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852411.
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Kanishk, Tanishk Nandal, Prince Tyagi, and Raj Kumar Singh. "Generation and Parameterization of Forced Isotropic Turbulent Flow Using Autoencoders and Generative Adversarial Networks." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69933.
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Abstract Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are used as model order reduction tools in applications of fluid mechanics by compressing input high-dimensional data using an encoder to map the input space into a lower-dimensional latent space. Whereas, generative models such as Variational Auto-encoders (VAEs) and Generative Adversarial Networks (GANs) are proving to be effective in generating solutions to chaotic models with high ‘randomness’ such as turbulent flows. In this study, forced isotropic turbulence flow is generated by parameterizing into some basic statistical characteristics. The models trained on pre-simulated data from dependencies on these characteristics and the flow generation is then affected by varying these parameters. The latent vectors pushed along the generator models like the decoders and generators contain independent entries which can be used to create different outputs with similar properties. The use of neural network-based architecture removes the need for dependency on the classical mesh-based Navier-Stoke equation estimation which is prominent in many CFD softwares.
Reports on the topic "Generative Adversarial Neural Networks (GAN's)"
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Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.
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Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, tokenized, and securely traded, ensuring ownership verification and automated royalty distribution. Simultaneously, AI-driven tools such as generative adversarial networks (GANs), neural networks, and natural language processing (NLP) models facilitate content generation, curation, and adaptive recommendations, enhancing creative workflows and fostering new artistic possibilities. This research report explores the synergies between AI and blockchain in the decentralized creative economy, analyzing their impact on digital rights protection, NFT marketplaces, decentralized publishing, AI-assisted music composition, and smart licensing models. Furthermore, it examines regulatory challenges, ethical considerations, and scalability limitations that need to be addressed for mainstream adoption. By integrating AI-powered automation with blockchain’s decentralized infrastructure, this study outlines a sustainable roadmap for secure, fair, and transparent digital creativity in the Web3 era. Keywords AI-powered creativity, blockchain-based digital ownership, decentralized innovation, generative AI, smart contracts, non-fungible tokens (NFTs), digital content authentication, AI-driven content generation, decentralized autonomous organizations (DAOs), intellectual property management, AI in art and music, Web3 creativity, tokenized digital assets, secure content monetization, ethical AI in blockchain, AI-assisted copyright protection, decentralized publishing, AI-powered music composition, blockchain scalability, AI for digital rights management.