Relevant bibliographies by topics / Generative adversarial networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Generative adversarial networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 23 November 2024

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Journal articles on the topic "Generative adversarial networks"

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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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Prozur, Vitalii. "Architecture and Formal-mathematical Justification of Generative Adversarial Networks." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 15 (July 15, 2024): 15–22. http://dx.doi.org/10.23939/sisn2024.15.015.

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The purpose of the work is to analyze the features of generative adversarial networks. The object of research is the process of machine learning algorithmization. The subject of the research is mathematical methods used in the generation of semantically related text. This article explores the architecture and mathematical justification of such a type of generative models as generative adversarial networks. Generative adversarial networks are a powerful tool in the field of artificial intelligence, capable of generating realistic data, including photos, videos, sounds, etc. The architecture of generative competition defines its structure, the interaction of components and a general description of the learning process. Mathematical justification, in turn, includes a theoretical analysis of the principles, algorithms and functions underlying these networks. The article examines the general architecture of generative adversarial networks, examines each of its components (namely, the two main network models – generator and discriminator, their input and output data vectors) and its role in the operation of the algorithm. The author also defined the mathematical principles of generative adversarial networks, focusing on game theory and optimization methods (in particular, special attention is paid to minimax and maximin problems, zero-sum game, saddle points, Nash equilibrium) used in their study. The cost function and the process of deriving it using the Nash equilibrium in a zero-sum game for generative adversarial networks are described, and the learning algorithm using the method of stochastic gradient descent and the mini-batch approach in the form of a pseudocode, its iterations, is visualized network architecture. Finally, the conclusion that generative adversarial networks is an effective tool for creating realistic and believable data samples based on the use of elements of game theory is substantiated. Due to the high quality of generated data, generative adversarial networks can be used in various fields, including: cyber security, medicine, commerce, science, art, etc.
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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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Cai, Zhipeng, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, and Yi Pan. "Generative Adversarial Networks." ACM Computing Surveys 54, no. 6 (July 2021): 1–38. http://dx.doi.org/10.1145/3459992.

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Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.
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Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial networks." Communications of the ACM 63, no. 11 (October 22, 2020): 139–44. http://dx.doi.org/10.1145/3422622.

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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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M. Alghazzawi, Daniyal, Syed Hamid Hasan, and Surbhi Bhatia. "Optimized Generative Adversarial Networks for Adversarial Sample Generation." Computers, Materials & Continua 72, no. 2 (2022): 3877–97. http://dx.doi.org/10.32604/cmc.2022.024613.

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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, Yueming, and Jianhua He. "Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology." International Journal of Distributed Systems and Technologies 15, no. 1 (August 29, 2024): 1–15. http://dx.doi.org/10.4018/ijdst.353305.

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Recent advancements in deep learning have popularized Generative Adversarial Networks for image generation. This study investigates integrating Generative Adversarial Networks technology into architectural design to empower architects in creating diverse, innovative, and practical designs. By analyzing architectural research, deep learning theory, and practical Generative Adversarial Networks applications, we substantiate the feasibility of using Generative Adversarial Networks for architectural design optimization. The generated architectural images exhibit significant diversity, innovation, and practicality, inspiring architects with numerous design possibilities. Overall, Generative Adversarial Networks technology not only expands design methodologies but also stimulates groundbreaking innovation in architectural practice. As technology progresses, Generative Adversarial Networks-based architectural design optimization shows promising potential for widespread adoption, heralding a new era of creativity and efficiency in architecture.
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Dissertations / Theses on the topic "Generative adversarial networks"

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Wang, Zesen. "Generative Adversarial Networks in Text Generation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264575.

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The Generative Adversarial Network (GAN) was firstly proposed in 2014, and it has been highly studied and developed in recent years. It has obtained great success in the problems that cannot be explicitly defined by a math equation such as generating real images. However, since the GAN was initially designed to solve the problem in a continuous domain (image generation, for example), the performance of GAN in text generation is developing because the sentences are naturally discrete (no interpolation exists between “hello" and “bye"). In the thesis, it firstly introduces fundamental concepts in natural language processing, generative models, and reinforcement learning. For each part, some state-of-art methods and commonly used metrics are introduced. The thesis also proposes two models for the random sentence generation and the summary generation based on context, respectively. Both models involve the technique of the GAN and are trained on the large-scale dataset. Due to the limitation of resources, the model is designed and trained as a prototype. Therefore, it cannot achieve the state-of-art performance. However, the results still show the promising performance of the application of GAN in text generation. It also proposes a novel model-based metric to evaluate the quality of summary referring both the source text and the summary. The source code of the thesis will be available soon in the GitHub repository: https://github.com/WangZesen/Text-Generation-GAN.
Det generativa motståndsnätverket (GAN) introducerades först 2014 och det har studerats samt utvecklats starkt under senare år. GAN har uppnått stor framgång för problem som inte kan definieras uttryckligen av en matematisk ekvation, som att generera riktiga bilder. Men eftersom GAN ursprungligen var utformat för att lösa problemet i en kontinuerlig domän (till exempel bildgenerering), utvecklas GAN:s prestanda i textgenerering eftersom meningarna är naturligt diskreta (ingen interpolering finns mellan “hej" och “hejdå"). I examensarbetet introduceras grundläggande begrepp i naturlig språkbearbetning, generativa modeller och förstärkningslärande. För varje del introduceras några bästa tillgängliga metoder och vanligt förekommande mätvärden. Examensarbetet föreslår också två modeller för slumpmässig meningsgenerering respektive sammanfattningsgenerering baserat på sammanhang. Båda modellerna involverar tekniken för GAN och är tränade på storskaliga datamängder. På grund av begränsningen av resurser är modellen designad och tränad som en prototyp. Därför kan den inte heller uppnå bästa möjliga prestanda. Resultaten visar ändå lovande prestanda för tillämpningen av GAN i textgenerering. Den föreslår också en ny modellbaserad metrik för att utvärdera kvaliteten på sammanfattningen som hänvisar både till källtexten och sammanfattningen. Examensarbetets källkod kommer snart att finnas tillgänglig i GitHubförvaret: https://github.com/WangZesen/Text-Generation-GAN.
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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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Berman, Alan. "Generative adversarial networks for fine art generation." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32458.

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Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing improved GAN results in other applications. This thesis investigates whether Wasserstein GANs and GANHack extensions to DCGANs can improve the quality of DCGAN-based fine art generation. There is also no accepted method of evaluating or comparing GANs for fine art generation. DCGAN's, Wasserstein GANs' and GANHack techniques' outputs on a modest computational budget were quantitatively and qualitatively compared to see which techniques showed improvement over DCGAN. A method for evaluating computer-generated fine art, HEART, is proposed to cover both the qualities of good human-created fine art and the shortcomings of computer-created fine art, and to include the cognitive and emotional impact as well as the visual appearance. Prominent GAN quantitative evaluation techniques were used to compare sample images these GANs produced on the MNIST, CIFAR-10 and Imagenet-1K image data sets. These results were compared with sample images these GANs produced on the above data sets, as well as on art data sets. A pilot study of HEART was performed with 20 users. Wasserstein GANs achieved higher visual quality outputs than the baseline DCGAN, as did the use of GANHacks, on all the fine art data sets and are thus recommended for use in future work on GAN-based fine art generation. The study also demonstrated that HEART can be used for the evaluation and comparison of art GANs, providing comprehensive, objective quality assessments which can be substantiated in terms of emotional and cognitive impact as well as visual appearance.
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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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Haiderbhai, Mustafa. "Generating Synthetic X-rays Using Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41092.

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We propose a novel method for generating synthetic X-rays from atypical inputs. This method creates approximate X-rays for use in non-diagnostic visualization problems where only generic cameras and sensors are available. Traditional methods are restricted to 3-D inputs such as meshes or Computed Tomography (CT) scans. We create custom synthetic X-ray datasets using a custom generator capable of creating RGB images, point cloud images, and 2-D pose images. We create a dataset using natural hand poses and train general-purpose Conditional Generative Adversarial Networks (CGANs) as well as our own novel network pix2xray. Our results show the successful plausibility of generating X-rays from point cloud and RGB images. We also demonstrate the superiority of our pix2xray approach, especially in the troublesome cases of occlusion due to overlapping or rotated anatomy. Overall, our work establishes a baseline that synthetic X-rays can be simulated using inputs such as RGB images and point cloud.
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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.
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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Graffieti, Gabriele. "Style Transfer with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17015/.

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This dissertation is focused on trying to use concepts from style transfer and image-to-image translation to address the problem of defogging. Defogging (or dehazing) is the ability to remove fog from an image, restoring it as if the photograph was taken during optimal weather conditions. The task of defogging is of particular interest in many fields, such as surveillance or self driving cars. In this thesis an unpaired approach to defogging is adopted, trying to translate a foggy image to the correspondent clear picture without having pairs of foggy and ground truth haze-free images during training. This approach is particularly significant, due to the difficult of gathering an image collection of exactly the same scenes with and without fog. Many of the models and techniques used in this dissertation already existed in literature, but they are extremely difficult to train, and often it is highly problematic to obtain the desired behavior. Our contribute was a systematic implementative and experimental activity, conducted with the aim of attaining a comprehensive understanding of how these models work, and the role of datasets and training procedures in the final results. We also analyzed metrics and evaluation strategies, in order to seek to assess the quality of the presented model in the most correct and appropriate manner. First, the feasibility of an unpaired approach to defogging was analyzed, using the cycleGAN model. Then, the base model was enhanced with a cycle perceptual loss, inspired by style transfer techniques. Next, the role of the training set was investigated, showing that improving the quality of data is at least as important as the utilization of more powerful models. Finally, our approach is compared with state-of-the art defogging methods, showing that the quality of our results is in line with preexisting approaches, even if our model was trained using unpaired data.
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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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Paget, Bryan. "An Introduction to Generative Adversarial Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39603.

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Daniel, Filippo <1995&gt. "Transfer learning with generative adversarial networks." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16989.

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Generative Adversarial Networks (GANs) emerged in recent years as the undiscussed SotA for image synthesis. This model leverages the recent successes of convolutional networks in the field of computer vision to learn the probability distribution of image datasets. Following the first proposal of GANs, many developments and usages of the models have been proposed. This thesis aims to review the evolution of the model and use one of the most recent variations to generate realistic portrait images with a targeted set of features. The usage of this model will be applied in a transfer learning approach, discussing the advantages and disadvantages from standard approaches. Furthermore, classical and deep computer vision tools will be used to edit and confirm the results obtained from the GAN model.
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Books on the topic "Generative adversarial networks"

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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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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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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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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 networks"

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Tomczak, Jakub M. "Generative Adversarial Networks." In Deep Generative Modeling, 159–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_7.

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Tomczak, Jakub M. "Generative Adversarial Networks." In Deep Generative Modeling, 201–15. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2_8.

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Mao, Xudong, and Qing Li. "Generative Adversarial Networks (GANs)." In Generative Adversarial Networks for Image Generation, 1–7. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6048-8_1.

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Salvaris, Mathew, Danielle Dean, and Wee Hyong Tok. "Generative Adversarial Networks." In Deep Learning with Azure, 187–208. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3679-6_8.

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Leili Mirtaheri, Seyedeh, and Reza Shahbazian. "Generative Adversarial Networks." In Machine Learning Theory to Applications, 158–70. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003119258-6.

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Paper, David. "Generative Adversarial Networks." In State-of-the-Art Deep Learning Models in TensorFlow, 243–63. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8_10.

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Zeng, Xiangming, and Liangqu Long. "Generative Adversarial Networks." In Beginning Deep Learning with TensorFlow, 553–99. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7915-1_13.

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Xiong, Momiao. "Generative Adversarial Networks." In Artificial Intelligence and Causal Inference, 109–50. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003028543-4.

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Adari, Suman Kalyan, and Sridhar Alla. "Generative Adversarial Networks." In Beginning Anomaly Detection Using Python-Based Deep Learning, 321–43. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0008-5_7.

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Cohen, Gilad, and Raja Giryes. "Generative Adversarial Networks." In Machine Learning for Data Science Handbook, 375–400. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24628-9_17.

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Conference papers on the topic "Generative adversarial networks"

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Tikas, Evangelos, Lazaros Alexios Iliadis, Sotirios Sotiroudis, Achilles Boursianis, Konstantinos-Iraklis D. Kokkinidis, Achilleas Papatheodorou, and Sotirios K. Goudos. "Human Blastocyst Image Generation Using Generative Adversarial Networks." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635904.

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Hayawi, Kadhim, Sakib Shahriar, and Hakim Hacid. "On Digital Art Generation Using Generative Adversarial Networks." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698003.

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Hu, Xufei, Ou Ye, and Zhenhua Yu. "A Method for Generating Speech Adversarial Examples Using Conditional Generative Adversarial Networks." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 538–41. IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743496.

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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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Krichen, Moez. "Generative Adversarial Networks." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306417.

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Ye, Yang. "Generative adversarial networks." In 2021 International Conference on Computer Vision and Pattern Analysis, edited by Ruimin Hu, Yang Yue, and Siting Chen. SPIE, 2022. http://dx.doi.org/10.1117/12.2626949.

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Liu, Dong, Yu Hong, Jianmin Yao, and Guodong Zhou. "Question Generation via Generative Adversarial Networks." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191871.

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Huang, Xun, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie. "Stacked Generative Adversarial Networks." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.202.

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Volkhonskiy, Denis, Ivan Nazarov, and Evgeny Burnaev. "Steganographic generative adversarial networks." In Twelfth International Conference on Machine Vision, edited by Wolfgang Osten and Dmitry P. Nikolaev. SPIE, 2020. http://dx.doi.org/10.1117/12.2559429.

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Nguyen, Khoa, Nghia Vu, Dung Nguyen, and Khoat Than. "Random Generative Adversarial Networks." In SoICT 2022: The 11th International Symposium on Information and Communication Technology. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3568562.3568589.

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Reports on the topic "Generative adversarial networks"

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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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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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