This repository provides the official implementation for InfoGAN, a generative adversarial network that learns interpretable representations by maximizing mutual information. It is intended for researchers and practitioners in deep learning and generative modeling seeking to understand and reproduce the paper's results.

How It Works

InfoGAN extends standard Generative Adversarial Networks (GANs) by introducing a novel objective function that maximizes the mutual information between a subset of the latent variables and the generated output. This encourages the latent variables to capture salient, interpretable features of the data distribution, such as object type, rotation, or translation.

Quick Start & Requirements

• Install: Clone the repository and use the provided Dockerfile or install dependencies manually.
• Prerequisites: TensorFlow development version (commit 79174a), prettytensor, progressbar, python-dateutil.
• Running MNIST: PYTHONPATH='.' python launchers/run_mnist_exp.py
• TensorBoard: tensorboard --logdir logs/mnist
• Docker: See README for detailed instructions.

Highlighted Details

• Reproduces key results from the InfoGAN paper.
• Focuses on learning interpretable latent representations.
• Includes an example for MNIST dataset.

Maintenance & Community

• Status: Archived, no updates expected.
• Community: No community links (Discord, Slack) are provided.

Licensing & Compatibility

• License: Not explicitly stated in the README.
• Compatibility: Requires a specific, older development version of TensorFlow, potentially limiting compatibility with modern TensorFlow ecosystems.

Limitations & Caveats

The project is archived and will not receive further updates. It requires a specific, older development version of TensorFlow, which may be difficult to set up and maintain. The license is not specified, which could impact commercial use.