Abstract
This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contrasting them with traditional generative models and elucidating the core adversarial mechanisms through illustrative Python examples. The text systematically addresses the mathematical and theoretical underpinnings including probability theory, statistics, and game theory providing a solid framework for understanding the objectives, loss functions, and optimisation challenges inherent to GAN training. Subsequent chapters review classic variants such as Conditional GANs, DCGANs, InfoGAN, and LAPGAN before progressing to advanced training methodologies like Wasserstein GANs, GANs with gradient penalty, least squares GANs, and spectral normalisation techniques. The book further examines architectural enhancements and task-specific adaptations in generators and discriminators, showcasing practical implementations in high resolution image generation, artistic style transfer, video synthesis, text to image generation and other multimedia applications. The concluding sections offer insights into emerging research trends, including self-attention mechanisms, transformer-based generative models, and a comparative analysis with diffusion models, thus charting promising directions for future developments in both academic and applied settings.
Abstract (translated)
这本书以对生成对抗网络（GANs）的基本原理和历史发展进行了详细的介绍开始，通过与传统的生成模型对比，并利用说明性的Python示例阐述了核心的对抗机制。文本系统地探讨了数学和理论基础，包括概率论、统计学和博弈论，为理解GAN训练的目标、损失函数以及优化挑战提供了一个坚实的基础框架。 随后的章节回顾了经典的变体，如条件生成对抗网络（Conditional GANs）、深度卷积生成对抗网络（DCGANs）、信息GAN（InfoGAN）和拉普拉斯生成对抗网络（LAPGAN），然后逐步过渡到更先进的训练方法，例如瓦瑟斯坦生成对抗网络（Wasserstein GANs）、具有梯度惩罚的GAN、最小二乘GAN以及谱归一化技术。本书进一步探讨了在生成器和判别器架构上的改进措施及特定任务适应性，展示了在高分辨率图像生成、艺术风格迁移、视频合成、文本到图像生成和其他多媒体应用中的实用实现。 最后的部分提供了关于新兴研究趋势的见解，包括自我注意机制（self-attention mechanisms）、基于变换器的生成模型以及与扩散模型的比较分析，从而勾勒出未来学术和实际应用场景中GAN发展的有前景方向。
URL
https://arxiv.org/abs/2502.04116
