Готові списки джерел за темами / GAN Generative Adversarial Network

Добірка наукової літератури з теми "GAN Generative Adversarial Network"

Автор: Grafiati
Опубліковано: 1 червня 2022

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Статті в журналах з теми "GAN Generative Adversarial Network"

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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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Анотація:
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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2

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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Анотація:
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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Naman and Sudha Narang, Chaudhary Sarimurrab, Ankita Kesari. "Human Face Generation using Deep Convolution Generative Adversarial Network." January 2021 7, no. 01 (January 29, 2021): 114–20. http://dx.doi.org/10.46501/ijmtst070127.

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Анотація:
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.
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4

Chi, Wanle, Yun Huoy Choo, and Ong Sing Goh. "Review of Generative Adversarial Networks in Image Generation." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 1 (January 20, 2022): 3–7. http://dx.doi.org/10.20965/jaciii.2022.p0003.

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Анотація:
Generative adversarial network (GAN) model generates and discriminates images using an adversarial competitive strategy to generate high-quality images. The implementation of GAN in different fields is helpful for generating samples that are not easy to obtain. Image generation can help machine learning to balance data and improve the accuracy of the classifier. This paper introduces the principles of the GAN model and analyzes the advantages and disadvantages of improving GANs. The applications of GANs in image generation are analyzed. Finally, the problems of GANs in image generation are summarized.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.

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Анотація:
Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.

Повний текст джерела
Анотація:
Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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7

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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Анотація:
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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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (March 2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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Анотація:
<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provide large coverage to achieve load balancing. By combing Generative Adversarial Network (GAN) with the ns-3 network simulator, this paper uses neural networks in TensorFlow to optimize load balancing of mobile networks, increase the data throughput and reduce the packet loss rate. In addition, to discuss the load balancing problem, we take the data produced by the ns-3 network simulator as the real data for GAN.</p> <p>&nbsp;</p>
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9

Tu, Jun, Willies Ogola, Dehong Xu, and Wei Xie. "Intrusion Detection Based on Generative Adversarial Network of Reinforcement Learning Strategy for Wireless Sensor Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 478–82. http://dx.doi.org/10.46300/9106.2022.16.58.

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Анотація:
Due to the wireless nature of wireless sensor networks (WSN), the network can be deployed in most of the unattended environment, which makes the networks more vulnerable for attackers who may listen to the traffic and inject their own nodes in the sensor network. In our work, we research on a novel machine learning algorithm on intrusion detection based on reinforcement learning (RL) strategy using generative adversarial network (GAN) for WSN which can automatically detect intrusion or malicious attacks into the network. We combine Actor-Critic Algorithm in RL with GAN in a simulated WSN. The GAN is employed as part of RL environment to generate fake data with possible attacks, which is similar to the real data generated by the sensor networks. Its main aim is to confuse the adversarial network into differentiating between the real and fake data with possible attacks. The results that is from the experiments are based on environment of GAN and Network Simulator 3 (NS3) illustrate that Actor-Critic&GAN algorithm enhances security of the simulated WSN by protecting the networks data against adversaries and improves on the accuracy of the detection.
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10

Han, Ligong, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, and Dimitris Metaxas. "Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10909–16. http://dx.doi.org/10.1609/aaai.v34i07.6723.

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Анотація:
Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website*.
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Дисертації з теми "GAN Generative Adversarial Network"

1

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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Анотація:
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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2

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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Анотація:
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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3

Eisenbeiser, Logan Ryan. "Latent Walking Techniques for Conditioning GAN-Generated Music." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/100052.

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Анотація:
Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Generating music is very difficult; components like long and short term structure present time complexity, which can be difficult for neural networks to capture. Additionally, the acoustics of musical features like harmonies and chords, as well as timbre and instrumentation require complex representations for a network to accurately generate them. Various techniques for both music representation and network architecture have been used in the past decade to address these challenges in music generation. The focus of this thesis extends beyond generating music to the challenge of controlling and/or conditioning that generation. Conditional generation involves an additional piece or pieces of information which are input to the generator and constrain aspects of the results. Conditioning can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored. This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact of this conditioning on the generated music.
Master of Science
Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Beyond simply generating music lies the challenge of controlling or conditioning that generation. Conditional generation can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored, especially for generative adversarial networks (GANs). This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact and effectiveness of this conditioning on the generated music.
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4

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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Анотація:
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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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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Анотація:
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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6

Ljung, Mikael. "Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301307.

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Анотація:
Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate images with high-quality. Due to the architecture’s success with images, it has been adapted to new domains, and this study examines its potential to synthesize financial tabular data. The study investigates a state-of-the-art model within tabular GANs, called CTGAN, together with two proposed ideas to enhance its generative ability. The results indicate that a modified training dynamic and a novel early stopping strategy improve the architecture’s capacity to synthesize data. The generated data presents realistic features with clear influences from its underlying dataset, and the inferred conclusions on subsequent analyses are similar to those based on the original data. Thus, the conclusion is that GANs has great potential to generate tabular data that can be considered a substitute for sensitive data, which could enable organizations to have more generous data sharing policies.
Med striktare förhållningsregler till hur data ska hanteras genom GDPR och PIPEDA har intresset för anonymiseringsmetoder för att censurera känslig data aktualliserats. En lovande teknik inom området återfinns i Generativa Motstridande Nätverk, en arkitektur som syftar till att generera data som återspeglar de statiska egenskaperna i dess underliggande dataset utan att äventyra datasubjektens integritet. Trots forskningsfältet unga ålder har man gjort stora framsteg i genereringsprocessen av så kallad syntetisk data, och numera finns det modeller som kan generera bilder av hög realistisk karaktär. Som ett steg framåt i forskningen har arkitekturen adopterats till nya domäner, och den här studien syftar till att undersöka dess förmåga att syntatisera finansiell tabelldata. I studien undersöks en framträdande modell inom forskningsfältet, CTGAN, tillsammans med två föreslagna idéer i syfte att förbättra dess generativa förmåga. Resultaten indikerar att en förändrad träningsdynamik och en ny optimeringsstrategi förbättrar arkitekturens förmåga att generera syntetisk data. Den genererade datan håller i sin tur hög kvalité med tydliga influenser från dess underliggande dataset, och resultat på efterföljande analyser mellan datakällorna är av jämförbar karaktär. Slutsatsen är således att GANs har stor potential att generera tabulär data som kan betrakatas som substitut till känslig data, vilket möjliggör för en mer frikostig delningspolitik av data inom organisationer.
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7

Sargent, Garrett Craig. "A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383.

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Zou, Xiaozhou. "Improve the Convergence Speed and Stability of Generative Adversarial Networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1309.

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Анотація:
In this thesis, we address two major problems in Generative Adversarial Networks (GAN), an important sub-field in deep learning. The first problem that we address is the instability in the training process that happens in many real-world problems and the second problem that we address is the lack of a good evaluation metric for the performance of GAN algorithms. To understand and address the first problem, three approaches are developed. Namely, we introduce randomness to the training process; we investigate various normalization methods; most importantly we develop a better parameter initialization strategy to help stabilize training. In the randomness techniques part of the thesis, we developed two randomness approaches, namely the addition of gradient noise and the batch random flipping of the results from the discrimination section of a GAN. In the normalization part of the thesis, we compared the performances of the z-score transform, the min-max normalization, affine transformations and batch normalization. In the most novel and important part of this thesis, we developed techniques to initialize the GAN generator section with parameters that can produce a uniform distribution on the range of the training data. As far as we are aware, this seemingly simple idea has not yet appeared in the extant literature, and the empirical results we obtain on 2-dimensional synthetic data show marked improvement. As to better evaluation metrics, we demonstrate a simple yet effective way to evaluate the effectiveness of the generator using a novel "overlap loss".
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Waldow, Walter E. "An Adversarial Framework for Deep 3D Target Template Generation." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1597334881614898.

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Birgersson, Anna, and Klara Hellgren. "Texture Enhancement in 3D Maps using Generative Adversarial Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162446.

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Анотація:
In this thesis we investigate the use of GANs for texture enhancement. To achievethis, we have studied if synthetic satellite images generated by GANs will improvethe texture in satellite-based 3D maps. We investigate two GANs; SRGAN and pix2pix. SRGAN increases the pixelresolution of the satellite images by generating upsampled images from low resolutionimages. As for pip2pix, the GAN performs image-to-image translation bytranslating a source image to a target image, without changing the pixel resolution. We trained the GANs in two different approaches, named SAT-to-AER andSAT-to-AER-3D, where SAT, AER and AER-3D are different datasets provided bythe company Vricon. In the first approach, aerial images were used as groundtruth and in the second approach, rendered images from an aerial-based 3D mapwere used as ground truth. The procedure of enhancing the texture in a satellite-based 3D map was dividedin two steps; the generation of synthetic satellite images and the re-texturingof the 3D map. Synthetic satellite images generated by two SRGAN models andone pix2pix model were used for the re-texturing. The best results were presentedusing SRGAN in the SAT-to-AER approach, in where the re-textured 3Dmap had enhanced structures and an increased perceived quality. SRGAN alsopresented a good result in the SAT-to-AER-3D approach, where the re-textured3D map had changed color distribution and the road markers were easier to distinguishfrom the ground. The images generated by the pix2pix model presentedthe worst result. As for the SAT-to-AER approach, even though the syntheticsatellite images generated by pix2pix were somewhat enhanced and containedless noise, they had no significant impact in the re-texturing. In the SAT-to-AER-3D approach, none of the investigated models based on the pix2pix frameworkpresented any successful results. We concluded that GANs can be used as a texture enhancer using both aerialimages and images rendered from an aerial-based 3D map as ground truth. Theuse of GANs as a texture enhancer have great potential and have several interestingareas for future works.
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Книги з теми "GAN Generative Adversarial Network"

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GANs in Action: Deep Learning with Generative Adversarial Networks. Manning Publications Company, 2019.

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Частини книг з теми "GAN Generative Adversarial Network"

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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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Huq, Mahmudul, and Rytis Maskeliunas. "Speech Enhancement Using Generative Adversarial Network (GAN)." In Hybrid Intelligent Systems, 273–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_26.

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Diao, Yufeng, Liang Yang, Xiaochao Fan, Yonghe Chu, Di Wu, Shaowu Zhang, and Hongfei Lin. "AFPun-GAN: Ambiguity-Fluency Generative Adversarial Network for Pun Generation." In Natural Language Processing and Chinese Computing, 604–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60450-9_48.

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Stamm, Matthew C., and Xinwei Zhao. "Anti-Forensic Attacks Using Generative Adversarial Networks." In Multimedia Forensics, 467–90. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_17.

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Анотація:
AbstractThe rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat posed by GAN-based anti-forensic attacks. GANs are a powerful machine learning framework that can be used to create realistic, but completely synthetic data. Researchers have recently shown that anti-forensic attacks can be built by using GANs to create synthetic forensic traces. While only a small number of GAN-based anti-forensic attacks currently exist, results show these early attacks are both effective at fooling forensic algorithms and introduce very little distortion into attacked images.
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Parvathi, R., and V. Pattabiraman. "Image Synthesis with Generative Adversarial Networks (GAN)." In Computer Vision and Recognition Systems, 239–50. New York: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003180593-12.

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Ouyang, Jiahong, Guanhua Wang, Enhao Gong, Kevin Chen, John Pauly, and Greg Zaharchuk. "Task-GAN: Improving Generative Adversarial Network for Image Reconstruction." In Machine Learning for Medical Image Reconstruction, 193–204. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33843-5_18.

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Kokate, Pradnyawant, Amit D. Joshi, and P. S. Tamizharasan. "An Empirical Comparison of Generative Adversarial Network (GAN) Measures." In Lecture Notes in Electrical Engineering, 1383–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_105.

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Bayramli, Bayram, Usman Ali, Te Qi, and Hongtao Lu. "FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Network." In Neural Information Processing, 3–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36708-4_1.

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Yu, Xiaoli, Yanyun Qu, and Ming Hong. "Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network." In Pattern Recognition and Information Forensics, 66–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05792-3_7.

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Тези доповідей конференцій з теми "GAN Generative Adversarial Network"

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Luo, Fuli, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, and Xu Sun. "Pun-GAN: Generative Adversarial Network for Pun Generation." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1336.

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Cheng, Adriel. "PAC-GAN: Packet Generation of Network Traffic using Generative Adversarial Networks." In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2019. http://dx.doi.org/10.1109/iemcon.2019.8936224.

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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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Анотація:
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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He, Dongxiao, Lu Zhai, Zhigang Li, Di Jin, Liang Yang, Yuxiao Huang, and Philip S. Yu. "Adversarial Mutual Information Learning for Network Embedding." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/459.

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Анотація:
Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on generative adversarial networks (GAN) (a promising deep learning technique), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a priori for network embedding. However, this strategy makes it difficult to distinguish the node representation from Gaussian distribution. Moreover, it does not make full use of the essential advantage of GAN (that is to adversarially learn the representation mechanism rather than the representation itself), leading to compromised performance of the method. To address this problem, we propose to use the adversarial idea on the representation mechanism, i.e. on the encoding mechanism under the framework of autoencoder. Specifically, we use the mutual information between node attributes and embedding as a reasonable alternative of this encoding mechanism (which is much easier to track). Additionally, we introduce another mapping mechanism (which is based on GAN) as a competitor into the adversarial learning system. A range of empirical results demonstrate the effectiveness of the proposed approach.
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Joshi, Amol S., Ali Dabouei, Jeremy Dawson, and Nasser M. Nasrabadi. "FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network." In 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021. http://dx.doi.org/10.1109/ijcb52358.2021.9484406.

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Huang, Jialu, Ying Huang, Yan-ting Lin, Zi-yang Liu, Yang Lin, and Wenhui Wu. "Does Generative Adversarial Network (GAN) help in SRAF image generation?" In 2021 International Workshop on Advanced Patterning Solutions (IWAPS). IEEE, 2021. http://dx.doi.org/10.1109/iwaps54037.2021.9671262.

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Huq, Mahmudul. "Enhancement of Alaryngeal Speech using Generative Adversarial Network (GAN)." In 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2021. http://dx.doi.org/10.1109/aiccsa53542.2021.9686759.

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Meddahi, Amar, Hassen Drira, and Ahmed Meddahi. "SIP-GAN: Generative Adversarial Networks for SIP traffic generation." In 2021 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2021. http://dx.doi.org/10.1109/isncc52172.2021.9615632.

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Heyrani Nobari, Amin, Wei Chen, and Faez Ahmed. "Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-69963.

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Анотація:
Abstract Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes’ attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space. For further information and code for this paper please refer to http://decode.mit.edu/projects/rangegan/.
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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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Анотація:
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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Звіти організацій з теми "GAN 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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