This repository provides a PyTorch implementation of the Semi-supervised Generative Adversarial Network (GAN) described in the paper "Improved Techniques for Training GANs." It targets researchers and practitioners in generative modeling and semi-supervised learning, offering a proven approach for achieving high accuracy on image datasets with limited labeled data.

How It Works

The implementation leverages a semi-supervised GAN architecture that incorporates feature matching. This technique encourages the generator to produce realistic fake samples within high-density regions of the feature space. By doing so, the fake data effectively splits the boundaries between different classes, enhancing the discriminative power of the classifier and improving semi-supervised learning performance.

Quick Start & Requirements

• Install using pip install torch==1.2 torchvision tensorboardX.
• Requires Python 3.7.
• GPU acceleration is recommended (python ImprovedGAN.py --cuda).
• Official quick-start: https://github.com/Sleepychord/ImprovedGAN-pytorch

Highlighted Details

• Achieves 98.5% accuracy on MNIST with only 100 labeled samples and 59,000 unlabeled samples after 100 epochs.
• Addresses exploding gradient issues found in OpenAI's original implementation by changing the generator's final layer from Sigmoid to Softplus.
• Utilizes Weight Normalization in network architectures, a key component for stability and performance.

Maintenance & Community

• Last updated November 27, 2019, with updates to PyTorch 1.2 and Python 3.7.
• Previous versions for PyTorch 0.3 and Python 2.7 are available in commit history.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The project was last updated in late 2019, and the README notes that previous attempts to reproduce expected results were challenging, suggesting potential stability or reproducibility issues that may have been addressed by the author's modifications.