Готові списки джерел за темами / GAN Generative Adversarial Network

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Добірка наукової літератури з теми "GAN Generative Adversarial Network"

Автор: Grafiati
Опубліковано: 1 червня 2022
Оновлено: 31 липня 2025

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Статті в журналах з теми "GAN Generative Adversarial Network"

1

Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (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 represen
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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 (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 likelih
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3

Bhandari, Basant Babu, Aakash Raj Dhakal, Laxman Maharjan, and Asmin Karki. "Nepali Handwritten Letter Generation using GAN." Journal of Science and Engineering 9 (December 31, 2021): 49–55. http://dx.doi.org/10.3126/jsce.v9i9.46308.

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Анотація:
The generative adversarial networks seem to work very effectively for training generative deep neural networks. The aim is to generate Nepali Handwritten letters using adversarial training in raster image format. Deep Convolutional generative network is used to generate Nepali handwritten letters. Proposed generative adversarial model that works on Devanagari 36 classes, each having 10,000 images, generates the Nepali Handwritten Letters that are similar to the real-life data-set of total size 360,000 images. The generated letters are obtained by simultaneously training the generator and discr
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4

Rajbeer, Kaur, Kaur Amanpreet, and Kaur Kirandeep. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. https://doi.org/10.5281/zenodo.13690760.

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Анотація:
The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.
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5

Huo, Lin, Huanchao Qi, Simiao Fei, Cong Guan, and Ji Li. "A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis." Computational Intelligence and Neuroscience 2022 (July 13, 2022): 1–21. http://dx.doi.org/10.1155/2022/7592258.

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Анотація:
As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedd
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6

Rajbeer Kaur, Amanpreet Kaur, and Kirandeep Kaur. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. http://dx.doi.org/10.30574/gjeta.2024.19.1.0057.

Повний текст джерела
Анотація:
The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.
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7

Huang, Wenqi, Lingyu Liang, Zhen Dai, et al. "Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN." Journal of Physics: Conference Series 2662, no. 1 (2023): 012009. http://dx.doi.org/10.1088/1742-6596/2662/1/012009.

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Анотація:
Abstract To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of
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Amir, Iqbal, Hamizan Suhaimi, Roslina Mohamad, Ezmin Abdullah, and Chuan-Hsian Pu. "Hybrid encryption based on a generative adversarial network." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 971. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp971-978.

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Анотація:
In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against dif
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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 (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 an
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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 (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 an
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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 us
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Yamazaki, Hiroyuki Vincent. "On Depth and Complexity of Generative Adversarial Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217293.

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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
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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 t
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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 gr
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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) model
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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 i
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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 initiali
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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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10

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, wher
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Книги з теми "GAN Generative Adversarial Network"

1

Walters, Greg, and John Hany. Hands-On Generative Adversarial Networks with Pytorch 1. x: Implement Next-Generation Neural Networks to Build Powerful GAN Models Using Python. Packt Publishing, Limited, 2019.

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2

Langr, Jakub, and Vladimir Bok. GANs in Action: Deep Learning with Generative Adversarial Networks. Manning Publications Co. LLC, 2019.

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3

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

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4

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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5

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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6

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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7

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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8

Ponnusamy, Sivaram, Jilali Antari, Swaminathan Kalyanaraman, and Pawan R. Bhaladhare. Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs). IGI Global, 2024.

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9

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Анотація:
Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Fahrerassistenzsysteme und automatisiertes Fahren. VDI Verlag, 2022. http://dx.doi.org/10.51202/9783181023945.

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Анотація:
Inhalt Pitch der Innovationen – Impulsvorträge im Plenummit anschließender Poster-Session Manipulation von Sensordaten aus Testfahrten zur Analyse und Bewertung implementierter Rückfalllösungen . . . . . . . . . . . . . .1 Sensortechnologien und Perzeption Radar Target Simulator – Key Technology for AV Development . . . . . . . . . . . 13 Künstliche Intelligenz (KI), Verhaltensplanung und Kooperation Realisierung einer querführenden Fahrerassistenzfunktion mithilfe von adaptiver Regelung und neuronalen Netzen . . .. . . . . . .27 Augmentation von Kameradaten mit Generative Adversarial Networks
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Частини книг з теми "GAN Generative Adversarial Network"

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Okadome, Takeshi. "GAN: Generative Adversarial Network." In Essentials of Generative AI. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0029-8_12.

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

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4

Kaddoura, Sanaa. "Your First GAN." In A Primer on Generative Adversarial Networks. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32661-5_2.

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Kaddoura, Sanaa. "Overview of GAN Structure." In A Primer on Generative Adversarial Networks. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32661-5_1.

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

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Khaled, Afifa, and Taher A. Ghaleb. "MRI-GAN: Generative Adversarial Network for Brain Segmentation." In Advances in Computer Graphics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50069-5_21.

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Kaur Khanuja, Harmeet, and Aarti Amod Agarkar. "Towards GAN Challenges and Its Optimal Solutions." In Generative Adversarial Networks and Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003203964-13.

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Diao, Yufeng, Liang Yang, Xiaochao Fan, et al. "AFPun-GAN: Ambiguity-Fluency Generative Adversarial Network for Pun Generation." In Natural Language Processing and Chinese Computing. 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. 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
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Тези доповідей конференцій з теми "GAN Generative Adversarial Network"

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Barua, Linkon, and Mohammed Jamal Uddin. "Exchange Rate Forecasting via Generative Adversarial Network (GAN)." In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS). IEEE, 2024. https://doi.org/10.1109/compas60761.2024.10796492.

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2

Ma, Haoran, Liao Ye, Fanjie Ruan, et al. "Demonstration of Quantum Generative Adversarial Network with a Silicon Photonic Chip." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jth2a.73.

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Анотація:
We demonstrated a hybrid quantum-classical generative adversarial network (GAN) with a silicon photonic chip capable of generating arbitrary 2-qubit states. The chip was successfully applied for classical distribution loading and MNIST image generation.
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Li, Qihui, Ruixin Kang, and Haodong Lu. "Syntactic-Semantic Graph Fusion Generative Adversarial Network: SSGF-GAN." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899591.

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Bianchi, Luiz A. C., Rafael C. Pregardier, Luis A. L. Silva, and Carlos R. P. dos Santos. "2Pack-GAN: Exploring Transfer Learning to Fine-Tune Generative Adversarial Networks for Network Packet Generation." In NOMS 2025-2025 IEEE Network Operations and Management Symposium. IEEE, 2025. https://doi.org/10.1109/noms57970.2025.11073639.

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Tong, Yanxin, Jiale Xu, Xuan Du, Jingzhou Huang, and Houpan Zhou. "SP-GAN: Cycle-Consistent Generative Adversarial Networks for Shadow Puppet Generation." In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2024. http://dx.doi.org/10.1109/cis-ram61939.2024.10672765.

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Raj, Akash, Chandni Mishra, Abhinav Bahuguna, Harshit Shukla, Satvik Vats, and Vikrant Sharma. "Colourization of Old Black-and-White Media Using GAN (Generative Adversarial Network)." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE). IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10674787.

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Sun, Yiyun. "Dugdale-GAN: Physical Dugdale Model Integrated Generative Adversarial Network for High-quality Crack Image Generation." In 2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2025. https://doi.org/10.1109/icpeca63937.2025.10928903.

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Luo, Fuli, Shunyao Li, Pengcheng Yang, et al. "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). 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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Khatiman, Muhammad Nur Aqmal, Asma Abu-Samah, Muhammad Amin Azman, Rosdiadee Nordin, and Nor Fadzilah Abdullah. "Generation of Synthetic 5G Network Dataset Using Generative Adversarial Network (GAN)." In 2023 IEEE 16th Malaysia International Conference on Communication (MICC). IEEE, 2023. http://dx.doi.org/10.1109/micc59384.2023.10419563.

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Звіти організацій з теми "GAN Generative Adversarial Network"

1

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

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2

Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

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Анотація:
Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
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3

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, 2023. http://dx.doi.org/10.21236/ad1207921.

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