This repository provides the code for the paper "Improved Techniques for Training GANs," focusing on enhancing Generative Adversarial Network training. It is intended for researchers and practitioners in deep learning and computer vision looking to reproduce or build upon the paper's findings.

How It Works

The project implements several key improvements to GAN training, including minibatch discrimination, feature matching, and historical averaging. These techniques aim to stabilize training and improve the quality of generated samples by providing more robust gradient signals and preventing mode collapse.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.x, TensorFlow 1.x, CUDA (for GPU acceleration).
• Setup: Requires downloading datasets (MNIST, SVHN, CIFAR10, ImageNet). Estimated setup time depends on dataset download and CUDA configuration.
• Links: Official Paper

Highlighted Details

• Implements minibatch discrimination for improved sample diversity.
• Includes feature matching to stabilize generator training.
• Provides code for experiments on MNIST, SVHN, CIFAR10, and ImageNet.

Maintenance & Community

• Status: Archived. Code is provided as-is with no expected updates.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Generally compatible with commercial use, but check specific dependencies for any potential restrictions.

Limitations & Caveats

The project is archived and uses TensorFlow 1.x, which is no longer actively supported and may present compatibility challenges with modern hardware and software stacks. No updates are expected.