Relevant bibliographies by topics / Generative Adversarial Network

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Generative Adversarial Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Generative Adversarial Network"

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Chandra, B. Yashas. "Building a Generative Adversarial Network for Image Synthesis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (July 20, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem36641.

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Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.
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Song, Wenyin, and Haibin Li. "Research on asphalt materials based on machine vision and generate adversarial networks." Highlights in Science, Engineering and Technology 52 (July 4, 2023): 119–24. http://dx.doi.org/10.54097/hset.v52i.8846.

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With the rapid development of artificial intelligence and deep learning, computer vision-oriented generative models have been widely used. Among them, the generation of adversarial network has the most far-reaching influence. To solve the problem of dynamic instability in the training of generative adversarial networks, this paper proposes a rapid construction method of generative adversarial networks based on hidden layer characterization. The generation process of the adversarial network is divided into two independent generation processes to generate the representation of the experience hidden layer and the final result respectively, so as to stably generate the training dynamics of the adversarial network and capture more data patterns. This method can effectively and stably generate the training dynamics of the adversarial network. Finally, theoretical analysis proves that this method can stably generate the training dynamics of adversarial network and reduce the difficulty of adversarial training. Large-scale experiments on multiple data sets demonstrate the effectiveness of the proposed method.
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Iranmanesh, Seyed Mehdi, and Nasser M. Nasrabadi. "HGAN: Hybrid generative adversarial network." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 8927–38. http://dx.doi.org/10.3233/jifs-201202.

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In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.
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Huo, Lin, Huanchao Qi, Simiao Fei, Cong Guan, and Ji Li. "A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis." Computational Intelligence and Neuroscience 2022 (July 13, 2022): 1–21. http://dx.doi.org/10.1155/2022/7592258.

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As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedding, and applied to solve fault diagnosis problems with insufficient data. By analyzing the time-domain characteristics of rolling bearing life cycle monitoring data in actual working conditions, the operation data are divided into three periods, and the construction and training of the generative adversarial network model are carried out. Data generated by adversarial are compared with the real data in the time domain and frequency domain, respectively, and the similarity between the generated data and the real data is verified.
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Imamverdiyev, Yadigar, and Firangiz Musayeva. "Analysis of generative adversarial networks." Problems of Information Technology 13, no. 1 (January 24, 2022): 20–27. http://dx.doi.org/10.25045/jpit.v13.i1.03.

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Recently, a lot of research has been done on the use of generative models in the field of computer vision and image classification. At the same time, effective work has been done with the help of an environment called generative adversarial networks, such as video generation, music generation, image synthesis, text-to-image conversion. Generative adversarial networks are artificial intelligence algorithms designed to solve the problems of generative models. The purpose of the generative model is to study the set of training patterns and their probable distribution. The article discusses generative adversarial networks, their types, problems, and advantages, as well as classification and regression, segmentation of medical images, music generation, best description capabilities, text image conversion, video generation, etc. general information is given. In addition, comparisons were made between the generative adversarial network algorithms analyzed on some criteria.
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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.

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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.
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Chang, Yeong-Hwa, Pei-Hua Chung, Yu-Hsiang Chai, and Hung-Wei Lin. "Color Face Image Generation with Improved Generative Adversarial Networks." Electronics 13, no. 7 (March 25, 2024): 1205. http://dx.doi.org/10.3390/electronics13071205.

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This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.
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Huang, Wenqi, Lingyu Liang, Zhen Dai, Shang Cao, Huanming Zhang, Xiangyu Zhao, Jiaxuan Hou, Hanju Li, Wenhao Ma, and Liang Che. "Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN." Journal of Physics: Conference Series 2662, no. 1 (December 1, 2023): 012009. http://dx.doi.org/10.1088/1742-6596/2662/1/012009.

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Abstract To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of scenario generation, the silhouette coefficient method is used to improve K-means for constructing a scenario reduction model. Finally, the case analysis shows that the proposed method can obtain typical renewable energy power scenarios with spatiotemporal correlation and provide a reference for the analysis of power system operation scenarios.
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Wang, Donghua, Li Dong, Rangding Wang, Diqun Yan, and Jie Wang. "Targeted Speech Adversarial Example Generation With Generative Adversarial Network." IEEE Access 8 (2020): 124503–13. http://dx.doi.org/10.1109/access.2020.3006130.

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Lee, Minhyeok, and Junhee Seok. "Controllable Generative Adversarial Network." IEEE Access 7 (2019): 28158–69. http://dx.doi.org/10.1109/access.2019.2899108.

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Dissertations / Theses on the topic "Generative Adversarial Network"

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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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Zeid, Baker Mousa. "Generation of Synthetic Images with Generative Adversarial Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15866.

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Machine Learning is a fast growing area that revolutionizes computer programs by providing systems with the ability to automatically learn and improve from experience. In most cases, the training process begins with extracting patterns from data. The data is a key factor for machine learning algorithms, without data the algorithms will not work. Thus, having sufficient and relevant data is crucial for the performance. In this thesis, the researcher tackles the problem of not having a sufficient dataset, in terms of the number of training examples, for an image classification task. The idea is to use Generative Adversarial Networks to generate synthetic images similar to the ground truth, and in this way expand a dataset. Two types of experiments were conducted: the first was used to fine-tune a Deep Convolutional Generative Adversarial Network for a specific dataset, while the second experiment was used to analyze how synthetic data examples affect the accuracy of a Convolutional Neural Network in a classification task. Three well known datasets were used in the first experiment, namely MNIST, Fashion-MNIST and Flower photos, while two datasets were used in the second experiment: MNIST and Fashion-MNIST. The results of the generated images of MNIST and Fashion-MNIST had good overall quality. Some classes had clear visual errors while others were indistinguishable from ground truth examples. When it comes to the Flower photos, the generated images suffered from poor visual quality. One can easily tell the synthetic images from the real ones. One reason for the bad performance is due to the large quantity of noise in the Flower photos dataset. This made it difficult for the model to spot the important features of the flowers. The results from the second experiment show that the accuracy does not increase when the two datasets, MNIST and Fashion-MNIST, are expanded with synthetic images. This is not because the generated images had bad visual quality, but because the accuracy turned out to not be highly dependent on the number of training examples. It can be concluded that Deep Convolutional Generative Adversarial Networks are capable of generating synthetic images similar to the ground truth and thus can be used to expand a dataset. However, this approach does not completely solve the initial problem of not having adequate datasets because Deep Convolutional Generative Adversarial Networks may themselves require, depending on the dataset, a large quantity of training examples.
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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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Vanhainen, Erik, and Johan Adamsson. "Generating Realistic Neuronal Morphologies in 3D using a Generative Adversarial Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301788.

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Neuronal morphology is primarily responsible for the structure of the connectivity among the neurons and is an important determinant for neuronal activity. This raises questions about the relationship between neuron shape and neuron function. To further investigate the structure-function relationship in neurons, extensive modelling with more morphological data is key. Digitally reconstructing neurons is tedious and requires a lot of manual labour and hence several generative methods have been proposed. However these generative models utilizes the current understanding of neuronal morphology, often by imposing a priori constraints, and thus may be biased or do not capture reality fully. We present an alternative technique using a Generative Adversarial Network that generates neurons without being constrained by current human understanding. The model was trained on digital reconstructions of pyramidal cells from rats and mice in a voxelized representation with dimensionality 1283. The results show that the model can generate objects that exhibit realistic neuronal features with a wide variety of shapes. Even though realistic feature are present in the generated objects they are often easily distinguishable from real neurons because of small discontinuous parts and noise in the complex arborizations. Nevertheless, this work can be seen as a proof of concept for generating realistic three dimensional morphologies in an unbiased manner.
Neuroners morfologier är primärt ansvariga för strukturen hos kopplingarna mellan neuroner och är en avgörande faktor för neuronaktivitet. Detta väcker frågor om sambandet mellan neuroners form och funktionalitet. För att undersöka detta samband är omfattande modellering med mycket morfologidata viktigt. Digital rekonstruktion av neuroner är omfattande och kräver mycket manuellt arbete. Av den anledning har flera generativa metoder föreslagits, dock bygger dessa metoder på vår nuvarande förståelse om neuroners morfologi som kan vara felaktig eller ofullständig. Vi föreslår en alternativ metod som med ett Generative Adversarial Network genererar neuroner utan att begränsas av vår nuvarande förståelse om neuroner. Modellen tränades på digitala rekonstruktioner av pyramidalceller från råttor och möss där varje neuron är representerad med 1283 voxlar. Resultaten visar att modellen kan generera objekt med realistiska neuronala särdrag och former. Även fast genererade objekt har realistiska former går de lätt att urskilja från riktiga neuroner på grund av små diskontinuerliga delar och brus i komplexa förgreningar. Detta arbete kan icke desto mindre ses som en grund till framtida arbete inom generering av tredimensionella nervceller utan mänsklig bias.
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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.
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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Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen great success in generating complex high-dimensional data, but less work has been done on their use for regression problems. This thesis presents experiments to better understand how conditional GANs can be used in probabilistic regression. Different versions of GANs are extended to the conditional case and evaluated on synthetic and real datasets. It is shown that conditional GANs can learn to estimate a wide range of different distributions and be competitive with existing probabilistic regression models.
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Li, Yuchuan. "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42774.

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Flame and smoke image processing and analysis could improve performance to detect smoke or fire and identify many complicated fire hazards, eventually to help firefighters to fight fires safely. Deep Learning applied to image processing has been prevailing in recent years among image-related research fields. Fire safety researchers also brought it into their studies due to its leading performance in image-related tasks and statistical analysis. From the perspective of input data type, traditional fire research is based on simple mathematical regressions or empirical correlations relying on sensor data, such as temperature. However, data from advanced vision devices or sensors can be analyzed by applying deep learning beyond auxiliary methods in data processing and analysis. Deep Learning has a bigger capacity in non-linear problems, especially in high-dimensional spaces, such as flame and smoke image processing. We propose a video-based real-time smoke and flame analysis system with deep learning networks and fire safety knowledge. It takes videos of fire as input and produces analysis and prediction for flashover of fire. Our system consists of four modules. The Color2IR Conversion module is made by deep neural networks to convert RGB video frames into InfraRed (IR) frames, which could provide important thermal information of fire. Thermal information is critically important for fire hazard detection. For example, 600 °C marks the start of a flashover. As RGB cameras cannot capture thermal information, we propose an image conversion module from RGB to IR images. The core of this conversion is a new network that we innovatively proposed: Dual-Attention Generative Adversarial Network (DAGAN), and it is trained using a pair of RGB and IR images. Next, Video Semantic Segmentation Module helps extract flame and smoke areas from the scene in the RGB video frames. We innovated to use synthetic RGB video data generated and captured from 3D modeling software for data augmentation. After that, a Video Prediction Module takes the RGB video frames and IR frames as input and produces predictions of the subsequent frames of their scenes. Finally, a Fire Knowledge Analysis Module predicts if flashover is coming or not, based on fire knowledge criteria such as thermal information extracted from IR images, temperature increase rate, the flashover occurrence temperature, and increase rate of lowest temperature. For our contributions and innovations, we introduce a novel network, DAGAN, by applying foreground and background attention mechanisms in the image conversion module to help reduce the hardware device requirement for flashover prediction. Besides, we also make use of combination of thermal information from IR images and segmentation information from RGB images in our system for flame and smoke analysis. We also apply a hybrid design of deep neural networks and a knowledge-based system to achieve high accuracy. Moreover, data augmentation is also applied on the Video Semantic Segmentation Module by introducing synthetic video data for training. The test results of flashover prediction show that our system has leading places quantitative and qualitative in terms of various metrics compared with other existing approaches. It can give a flashover prediction as early as 51 seconds with 94.5% accuracy before it happens.
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Rinnarv, Jonathan. "GANChat : A Generative Adversarial Network approach for chat bot learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278143.

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Recently a new method for training generative neural networks called Generative Adversarial Networks (GAN) has shown great results in the computer vision domain and shown potential in other generative machine learning tasks as well. GAN training is an adversarial training method where two neural networks compete and attempt to outperform each other, and in the process they both learn. In this thesis the effectiveness of GAN training is tested on conversational agents also called chat bots. To test this, current state-of-the-art training methods such as Maximum Likelihood Estimation (MLE) models are compared with GAN method trained models. Model performance was measured by closeness of the model distribution from the target distribution after training. This thesis shows that the GAN method performs worse the MLE in some scenarios but can outperform MLE in some cases.
Nyligen har en ny metod för att träna generativa neurala nätverk kallad Generative Adversarial Networks (GAN) visat bra resultat inom datorseendedomänen och visat potential inom andra maskininlärningsområden också GAN-träning är en träningsmetod där två neurala nätverk tävlar och försöker överträffa varandra, och i processen lär sig båda. I detta examensarbete har effektiviteten av GAN-träning testats på konversationsagenter, som också kallas Chat bots. För att testa det här jämfördes modeller tränade med nuvarande state-of- the-art träningsmetoder, så som Maximum likelihood-metoden (ML), med GAN-tränade modeller. Modellernas prestation mättes genom distans från modelldistribution till måldistribution efter träning. Det här examensarbetet visar att GAN-metoden presterar sämre än ML-metoden i vissa scenarier men kan överträffa ML i vissa fall.
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Cabezas, Rodríguez Juan Pablo. "Generative adversarial network based model for multi-domain fault diagnosis." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170996.

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Memoria para optar al título de Ingeniero Civil Mecánico
Con el uso de las redes neuronal profundas ganando terreno en el área de PHM, los sensores disminuyendo progresivamente su precio y mejores algoritmos, la falta de datos se ha vuelto un problema principal para los modelos enfocados en datos. Los datos etiquetados y aplicables a escenarios específicos son, en el mejor de los casos, escasos. El objetivo de este trabajo es desarrollar un método para diagnosticas el estado de un rodamiento en situaciones con datos limitados. Hoy en día la mayoría de las técnicas se enfocan en mejorar la precisión del diagnóstico y en estimar la vida útil remanente en componentes bien documentados. En el presente, los métodos actuales son ineficiente en escenarios con datos limitados. Se desarrolló un método en el cual las señales vibratorias son usadas para crear escalogramas y espectrogramas, los cuales a su vez se usan para entrenar redes neuronales generativas y de clasificación, en función de diagnosticar un set de datos parcial o totalmente desconocido, en base a uno conocido. Los resultados se comparan con un método más sencillo en el cual la red para clasificación es entrenada con el set de datos conocidos y usada directamente para diagnosticar el set de datos desconocido. El Case Western Reserve University Bearing Dataset y el Machine Failure Prevention Technology Bearing Dataset fueron usados como datos de entrada. Ambos sets se usaron como conocidos tanto como desconocidos. Para la clasificación una red neuronal convolucional (CNN por sus siglas en inglés) fue diseñada. Una red adversaria generativa (GAN por sus siglas en inglés) fue usada como red generativa. Esta red fue basada en una introducida en el paper StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Los resultados fueron favorables para la red CNN mientras que fueron -en general- desfavorables para la red GAN. El análisis de resultados sugiere que la función de costo es inapropiada para el problema propuesto. Las conclusiones dictaminan que la traducción imagen-a-imagen basada en la función ciclo no funciona correctamente en señal vibratorias para diagnóstico de rodamientos. With the use of deep neural networks gaining notoriety on the prognostics & health management field, sensors getting progressively cheaper and improved algorithms, the lack of data has become a major issue for data-driven models. Data which is labelled and applicable for specific scenarios is scarce at best. The purpose of this works is to develop a method to diagnose the health state of a bearing on limited data situations. Now a days most techniques focus on improving accuracy for diagnosis and estimating remaining useful life on well documented components. As it stands, current methods are ineffective on limited data scenarios. A method was developed were in vibration signals are used to create scalograms and spectrograms, which in turn are used to train generative and classification neural networks with the goal of diagnosing a partially or totally unknown dataset based on a fully labelled one. Results were compared to a simpler method in which a classification network is trained on the labelled dataset to diagnose the unknown dataset. As inputs the Case Western Reserve University Bearing Dataset (CWR) and the Society for Machine Failure Prevention Technology Bearing Dataset. Both datasets are used as labelled and unknown. For classification a Convolutional Neural Network (CNN) is designed. A Generative Adversarial Network (GAN) is used as generative model. The generative model is based of a previous paper called StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Results were favourable for the CNN network whilst generally negative for the GAN network. Result analysis suggests that the cost function is unsuitable for the proposed problem. Conclusions state that cycle based image-to-image translation does not work correctly on vibration signals for bearing diagnosis.
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Desentz, Derek. "Partial Facial Re-imaging Using Generative Adversarial Networks." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622122813797895.

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Books on the topic "Generative Adversarial Network"

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Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6048-8.

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Raut, Roshani, Pranav D Pathak, Sachin R Sakhare, and Sonali Patil. Generative Adversarial Networks and Deep Learning. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003203964.

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Kaddoura, Sanaa. A Primer on Generative Adversarial Networks. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32661-5.

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Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Springer Singapore Pte. Limited, 2022.

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Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Springer Singapore Pte. Limited, 2021.

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Valle, Rafael. Hands-On Generative Adversarial Networks with Keras: Your Guide to Implementing Next-Generation Generative Adversarial Networks. Packt Publishing, Limited, 2019.

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Kaddoura, Sanaa. Primer on Generative Adversarial Networks. Springer International Publishing AG, 2023.

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Generative Adversarial Networks in Practice. CRC Press LLC, 2024.

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Generative Adversarial Networks in Practice. CRC Press LLC, 2023.

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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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Book chapters on the topic "Generative Adversarial Network"

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Yalçın, Orhan Gazi. "Generative Adversarial Network." In Applied Neural Networks with TensorFlow 2, 259–84. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6513-0_12.

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Rakesh, K., and V. Uma. "Generative Adversarial Network." In Artificial Intelligence (AI), 131–48. First edition. | Boca Raton : CRC Press, 2021. |: CRC Press, 2021. http://dx.doi.org/10.1201/9781003005629-7.

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Miyato, Takeru, and Masanori Koyama. "Generative Adversarial Network (GAN)." In Computer Vision, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_860-1.

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Miyato, Takeru, and Masanori Koyama. "Generative Adversarial Network (GAN)." In Computer Vision, 508–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_860.

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Tony, Suman Maria, and S. Sasikumar. "Generative Adversarial Network for Music Generation." In Lecture Notes in Electrical Engineering, 109–19. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9885-9_9.

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Ghayoumi, Mehdi. "Conditional Generative Adversarial Network (cGAN)." In Generative Adversarial Networks in Practice, 258–313. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-9.

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Ghayoumi, Mehdi. "Cycle Generative Adversarial Network (CycleGAN)." In Generative Adversarial Networks in Practice, 314–40. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-10.

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Ghayoumi, Mehdi. "Wasserstein Generative Adversarial Network (WGAN)." In Generative Adversarial Networks in Practice, 401–35. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-13.

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Teoh, Teik Toe, and Zheng Rong. "Deep Convolutional Generative Adversarial Network." In Machine Learning: Foundations, Methodologies, and Applications, 289–301. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8615-3_18.

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Jaiswal, Ayush, Wael AbdAlmageed, Yue Wu, and Premkumar Natarajan. "CapsuleGAN: Generative Adversarial Capsule Network." In Lecture Notes in Computer Science, 526–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11015-4_38.

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Conference papers on the topic "Generative Adversarial Network"

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Chen, Yijia. "Speech Generation by Generative Adversarial Network." In 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2021. http://dx.doi.org/10.1109/icbase53849.2021.00086.

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Wang, Chaoyue, Chaohui Wang, Chang Xu, and Dacheng Tao. "Tag Disentangled Generative Adversarial Network for Object Image Re-rendering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/404.

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In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.
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Mahdizadehaghdam, Shahin, Ashkan Panahi, and Hamid Krim. "Sparse Generative Adversarial Network." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00369.

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Sharpe, Conner, and Carolyn Conner Seepersad. "Topology Design With Conditional Generative Adversarial Networks." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97833.

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Abstract Deep convolutional neural networks have gained significant traction as effective approaches for developing detailed but compact representations of complex structured data. Generative networks in particular have become popular for their ability to mimic data distributions and allow further exploration of them. This attribute can be utilized in engineering design domains, in which the data structures of finite element meshes for analyzing potential designs are well suited to the deep convolutional network approaches that are being developed at a rapid pace in the field of image processing. This paper explores the use of conditional generative adversarial networks (cGANs) as a means of generating a compact latent representation of structures resulting from classical topology optimization techniques. The constraints and contextual factors of a design problem, such as mass fraction, material type, and load location, can then be specified as input conditions to generate potential topologies in a directed fashion. The trained network can be used to aid concept generation, such that engineers can explore a variety of designs relevant to the problem at hand with ease. The latent variables of the generator can also be used as design parameters, and the low dimensionality enables tractable computational design without analytical sensitivities. This paper demonstrates these capabilities and discusses avenues for further developments that would enable the engineering design community to further leverage generative machine learning techniques to their full potential.
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Zhang, Qiang, Jibin Yang, Xiongwei Zhang, and Tieyong Cao. "Generating Adversarial Examples in Audio Classification with Generative Adversarial Network." In 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9886154.

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Qin, Liwen, Xiaoyong Yu, Yuteng Luo, Shan Li, and Yangjun Zhou. "Generative Adversarial Networks-based Distribution Network Operation Scenario Generation." In 2022 Power System and Green Energy Conference (PSGEC). IEEE, 2022. http://dx.doi.org/10.1109/psgec54663.2022.9881069.

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Manocchio, Liam Daly, Siamak Layeghy, and Marius Portmann. "FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks." In 2021 IEEE 24th International Conference on Computational Science and Engineering (CSE). IEEE, 2021. http://dx.doi.org/10.1109/cse53436.2021.00033.

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Ji, Y., Y. Dai, K. Zhao, and S. Li. "Generative adversarial network for image deblurring using generative adversarial constraint loss." In 14th International FLINS Conference (FLINS 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811223334_0141.

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Dai, Guoxian, Jin Xie, and Yi Fang. "Metric-based Generative Adversarial Network." In MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123266.3123334.

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Sun, Yiwei, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. "MEGAN: A Generative Adversarial Network for Multi-View Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/489.

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Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.
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Reports on the topic "Generative Adversarial Network"

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Ellis, John. miniGAN: A Generative Adversarial Network proxy application. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1763585.

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McKee, Philip, and Jeffrey Lloyd. A Generative Adversarial Network Approach with a Random Patch Discriminator to Generate 3D Synthetic Microstructures Containing Second Phase Particles. DEVCOM Army Research Laboratory, August 2023. http://dx.doi.org/10.21236/ad1207921.

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Martinez, Matthew, and Olivia Heiner. Conditional Generative Adversarial Networks for Solving Heat Transfer Problems. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1673172.

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Athey, Susan, Guido Imbens, Jonas Metzger, and Evan Munro. Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations. Cambridge, MA: National Bureau of Economic Research, December 2019. http://dx.doi.org/10.3386/w26566.

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Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen, and Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.
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