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Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
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| 2026-07-31 | 1033 |
| 2026-08-05 | 1034 |
| 2026-08-06 | 1034 |
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# GANs in Action — Code Companion <p align="left"> <img src="https://img.shields.io/badge/Python-3.6+-blue.svg" alt="Python 3.6+"> <img src="https://img.shields.io/badge/TensorFlow-1.8.0+-orange.svg" alt="TensorFlow 1.8.0+"> <img src="https://img.shields.io/badge/Keras-2.1.6+-red.svg" alt="Keras 2.1.6+"> <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License MIT"> </p> This is the official companion repository to the book **[GANs in Action: Deep Learning with Generative Adversarial Networks](https://www.manning.com/books/gans-in-action)** by Jakub Langr and Vladimir Bok, published by Manning Publications. This repo lets you reproduce, study, and extend every hands‑on example from the book. The notebooks walk through every major variant in the GAN family, from the original **vanilla GAN** to **CycleGAN**, using Keras/TensorFlow. ## 📚 Table of Contents - [Overview](#overview) - [Repository Structure](#repository-structure) - [Canonical GAN Papers](#canonical-gan-papers) - [Getting Started](#getting-started) - [Chapter Implementations](#chapter-implementations) - [Educational Resources](#educational-resources) - [Best Practices](#best-practices) - [Community Resources](#community-resources) - [Citation](#citation) - [License](#license) ## 🎯 Overview This repository contains practical implementations of various Generative Adversarial Network architectures discussed in the book "GANs in Action". Each chapter includes Jupyter notebooks with fully functional code examples that demonstrate key concepts and techniques in GAN development. ### What You'll Learn - Fundamental concepts of generative modeling and adversarial training - Implementation of various GAN architectures from scratch - Best practices for training stable GANs - Real-world applications of GANs - Advanced techniques for improving GAN performance ## 📂 Repository Structure ``` gans-in-action/ ├── chapter-2/ # Autoencoders ├── chapter-3/ # Vanilla GAN ├── chapter-4/ # Deep Convolutional GAN (DCGAN) ├── chapter-6/ # Progressive GAN ├── chapter-7/ # Semi-Supervised GAN ├── chapter-8/ # Conditional GAN ├── chapter-9/ # CycleGAN ├── chapter-10/ # Adversarial examples └── requirements.txt # Python dependencies ``` ## 📄 Canonical GAN Papers Each implementation in this repository is based on groundbreaking research. Here are the canonical papers for each GAN architecture covered: ### Original GAN (Chapter 3) **Paper:** [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) **Authors:** Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio **Year:** 2014 **Key Contribution:** Introduced the foundational GAN framework with adversarial training between generator and discriminator networks. ### Deep Convolutional GAN - DCGAN (Chapter 4) **Paper:** [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) **Authors:** Alec Radford, Luke Metz, Soumith Chintala **Year:** 2015 **Key Contribution:** Established architectural guidelines for stable GAN training using convolutional networks. ### Progressive GAN (Chapter 6) **Paper:** [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196) **Authors:** Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen **Year:** 2017 **Key Contribution:** Introduced progressive training methodology for generating high-resolution images. ### Semi-Supervised GAN (Chapter 7) **Paper:** [Semi-Supervised Learning with Generative Adversarial Networks](https://arxiv.org/abs/1606.01583) **Authors:** Augustus Odena **Year:** 2016 **Key Contribution:** Extended GANs for semi-supervised learning by modifying the discriminator to output class labels. ### Conditional GAN - CGAN (Chapter 8) **Paper:** [Conditional
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matched fp:c3112cf918fa386d, llm:Repository topics and description: 'gan, gans, generative-adversarial-network, conditional-gan, cycle-gan, dcgan, deep-learning' and README: 'practical implementations of various Generative Adversarial Network architectures... Jupyter notebooks with fully functional code examples'
matched fp:c3112cf918fa386d, llm:Repository topics and description: 'gan, gans, generative-adversarial-network, conditional-gan, cycle-gan, dcgan, deep-learning' and README: 'practical implementations of various Generative Adversarial Network architectures... Jupyter notebooks with fully functional code examples'
matched fp:c3112cf918fa386d, llm:Repository topics and description: 'gan, gans, generative-adversarial-network, conditional-gan, cycle-gan, dcgan, deep-learning' and README: 'practical implementations of various Generative Adversarial Network architectures... Jupyter notebooks with fully functional code examples'