Handwriting Profiling using Generative Adversarial Networks

Ghosh, Arna; Bhattacharya, Biswarup; Chowdhury, Somnath Basu Roy

doi:10.48550/arxiv.1611.08789

Cited by 3 publications

(3 citation statements)

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“…Stochastic game [45] Stackelberg game [46,47] Bi-affine game [48] Modified Learning Method No regret learning [10,49,50] Fictitious play [27] Federated learning [51,52] Reinforcement learning [4,[53][54][55][56][57][58][59][60][61][62][63] Modified Architecture Multiple generators, One discriminator [46,[64][65][66][67] One generator, Multiple discriminators [60,[68][69][70][71][72] Multiple generators, Multiple discriminators [51,66,73] One generator, One discriminator, One classifier [4,74] One generator, One discriminator, One RL agent [58,59,75,76]…”

Section: Modified Game Modelmentioning

confidence: 99%

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Games of GANs: game-theoretical models for generative adversarial networks

et al. 2023

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Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game-theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative models and improve the GAN's performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, Games of GANs and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each class. Finally, we discuss the remaining challenges in this field and present future research directions.

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Section: Modified Game Modelmentioning

confidence: 99%

“…Ghosh et al in [61] used GANs for learning the handwriting of an entity and combine it with reinforcement learning techniques to achieve faster learning. The generator can generate words looking similar to the reference word, and the discriminator network can be used as an OCR (optical character recognition) system.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Games of GANs: game-theoretical models for generative adversarial networks

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Ghosh et al in [61] use GANs for learning the handwriting of an entity and combine it with reinforcement learning techniques to achieve faster learning. The generator can generate words looking similar to the reference word, and the discriminator network can be used as an OCR (optical character recognition) system.…”

Section: Md-ganmentioning

confidence: 99%

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Moghadam¹,

Boroomand²,

Barzegar-Jalali³

et al. 2021

Preprint

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Generative Adversarial Network, as a promising research direction in the AI community, recently attracts considerable attention due to its ability to generating high-quality realistic data. GANs are a competing game between two neural networks trained in an adversarial manner to reach a Nash equilibrium. Despite the improvement accomplished in GANs in the last years, there remain several issues to solve. In this way, how to tackle these issues and make advances leads to rising research interests. This paper reviews literature that leverages the game theory in GANs and addresses how game models can relieve specific generative models' challenges and improve the GAN's performance. In particular, we firstly review some preliminaries, including the basic GAN model and some game theory backgrounds. After that, we present our taxonomy to summarize the state-of-the-art solutions into three significant categories: modified game model, modified architecture, and modified learning method. The classification is based on the modifications made in the basic model by the proposed approaches from the game-theoretic perspective. We further classify each category into several subcategories. Following the proposed taxonomy, we explore the main objective of each class and review the recent work in each group. Finally, we discuss the remaining challenges in this field and present the potential future research topics.

show abstract

Games of GANs: Game-Theoretical Models for Generative Adversarial Networks

Moghadam

Boroomand

Jalali

et al. 2022

Preprint

View full text Add to dashboard Cite

Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game-theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative models and improve the GAN's performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each class. Finally, we discuss the remaining challenges in this field and present future research directions.

show abstract

Handwriting Profiling using Generative Adversarial Networks

Cited by 3 publications

References 0 publications

Games of GANs: game-theoretical models for generative adversarial networks

Games of GANs: game-theoretical models for generative adversarial networks

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Games of GANs: Game-Theoretical Models for Generative Adversarial Networks

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