Generative Adversarial Networks
May 1, 2024
Updated May 9, 2025
20 minute read
Generative Adversarial Networks, or GANs, represent a fascinating and powerful class of machine learning models. At a high level, a GAN is a system where two neural networks, known as the generator and the discriminator, compete with each other. The generator's job is to create new data instances that mimic a given dataset, while the discriminator's role is to distinguish between the real data and the fake data produced by the generator. This adversarial process drives both networks to improve, ultimately resulting in a generator that can produce highly realistic synthetic data.
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Reading list
We've selected 26 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Generative Adversarial Networks.
Provides a comprehensive overview of GANs, covering the theoretical foundations, algorithms, and applications. It is written by the pioneers of GANs and is considered the definitive guide to the topic.
The second edition of Foster's book includes updated content and covers recent advancements in generative AI, including more on GANs and related models like diffusion models and Transformers. It's highly relevant for contemporary topics in the field.
Provides a collection of case studies on the use of GANs in a variety of applications, including computer vision, natural language processing, and healthcare. It is written by a leading researcher in the field.
Provides a practical, hands-on approach to building and training GANs using Keras. It's well-suited for those with some deep learning basics and Python skills who want to implement GANs. It serves as a useful reference for practical applications and troubleshooting.
Covers generative AI using Python and TensorFlow 2, and would include GANs as a key generative model. It's relevant for those interested in implementing GANs with a focus on a widely used framework and contemporary practices in generative AI.
This project-based book allows readers to learn about GANs by implementing various architectures using TensorFlow and Keras. It's valuable for those who learn best by doing and want to explore different GAN applications. It serves as a practical guide with code examples.
Offers a practical introduction to GANs using the PyTorch framework, which is popular in research and academia. It's suitable for those familiar with PyTorch or looking to learn GAN implementation using this library. It covers core concepts and building various GAN models.
Provides a practical guide to using GANs for data science tasks, such as data augmentation and image generation. It includes numerous code examples and case studies.
这本书由著名的荷兰计算机科学家马克斯·韦林撰写，涵盖了GANs的算法和应用。非常适合想要深入理解GANs的读者。
Focusing on implementation with Keras, this book offers step-by-step instructions and practical projects for building GANs. It's suitable for those with some deep learning experience looking for hands-on practice with a popular framework.
Provides a more theoretical and comprehensive look at deep generative models, including but not limited to GANs. It's suitable for graduate students and researchers who want to understand the mathematical foundations and a wider range of generative techniques.
While not exclusively about GANs, this book covers fundamental deep learning concepts and architectures through case studies. It can help solidify the understanding of neural networks and training techniques essential for working with GANs.
Covers the fundamental concepts of deep learning, which are prerequisites for understanding GANs. It's suitable for those who need to build a strong base in deep learning before specializing in generative models.
Given that many GAN applications are in computer vision, this book provides relevant background and techniques in that domain. It would be a useful supplementary read for understanding the image-based applications of GANs.
A widely respected classic in the field of machine learning, this book provides essential background in probability, statistics, and machine learning concepts that are fundamental to understanding GANs at a deeper level. It's more of a foundational text than a GAN-specific book.
Aims to build an intuitive understanding of deep learning concepts from the ground up. It could be helpful for learners who are new to deep learning and want a more accessible introduction before tackling the complexities of GANs.
Takes a visual approach to explaining deep learning concepts, which can be very helpful for building intuition. While it may not cover GANs in detail, a strong visual understanding of neural networks is beneficial for grasping GAN architectures.
While not directly about GANs in a deep learning context, this book explores the broader concept of generative design. It could provide a different perspective on generating creative outputs, which key application of GANs, and might inspire interdisciplinary approaches.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/an4yph/generative
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