Generative Adversarial Networks
Generative Adversarial Networks
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DOI：
10.1109/icccnt56998.2023.10306417
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Gilad Cohen;Raja Giryes
中科院分区：
文献类型：
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作者：
Gilad Cohen;Raja Giryes
生成对抗网络（GAN）是一种深度学习技术，在生成逼真的图像、视频和其他类型的数据方面取得了显着的成功。本文提供了一个全面的GAN指南，涵盖了它们的架构，损失函数，训练方法，应用程序，评估指标，挑战和未来方向。我们开始介绍GAN及其历史发展，然后回顾背景和相关工作。然后，我们提供了GAN架构的详细概述，包括发电机和以太网网络，并讨论了关键的设计选择和变化。接下来，我们将回顾GAN中使用的损失函数，包括最初的极大极小目标，以及最近的方法。Wasserstein距离和梯度惩罚然后我们深入研究GAN的训练，讨论常见技术等交替优化、小批量鉴别和光谱归一化。我们还提供了跨领域GAN的各种应用的调查。此外，我们还回顾了用于评估GAN生成数据的多样性和质量的评估指标。此外，我们还讨论了GAN中的挑战和开放问题，包括模式崩溃、训练不稳定性和伦理考虑。最后，我们对GAN研究的未来方向进行了展望，包括提高可扩展性、开发新架构、整合领域知识以及探索新应用。总的来说，本文是GAN的综合指南，为该领域的研究人员和实践者提供了理论和实践见解。
Generative Adversarial Networks (GANs) are a type of deep learning techniques that have shown remarkable success in generating realistic images, videos, and other types of data. This paper provides a comprehensive guide to GANs, covering their architecture, loss functions, training methods, applications, evaluation metrics, challenges, and future directions. We begin with an introduction to GANs and their historical development, followed by a review of the background and related work. We then provide a detailed overview of the GAN architecture, including the generator and discriminator networks, and discuss the key design choices and variations. Next, we review the loss functions utilized in GANs, including the original minimax objective, as well as more recent approaches s.a. Wasserstein distance and gradient penalty. We then delve into the training of GANs, discussing common techniques s.a. alternating optimization, minibatch discrimination, and spectral normalization. We also provide a survey of the various applications of GANs across domains. In addition, we review the evaluation metrics utilized to assess the diversity and quality of GAN-produced data. Furthermore, we discuss the challenges and open issues in GANs, including mode collapse, training instability, and ethical considerations. Finally, we provide a glimpse into the future directions of GAN research, including improving scalability, developing new architectures, incorporating domain knowledge, and exploring new applications. Overall, this paper serves as a comprehensive guide to GANs, providing both theoretical and practical insights for researchers and practitioners in the field.