This repository provides a PyTorch implementation of Vector Quantized Variational Autoencoders (VQ-VAEs), a generative model for learning discrete representations of data. It's suitable for researchers and practitioners interested in neural representation learning and generative modeling, offering a clear path to reproducing results from a course project.

How It Works

The implementation focuses on the VQ-VAE architecture, which uses a vector quantization layer to learn a discrete latent space. This approach is advantageous for its ability to capture complex data distributions and generate high-quality samples, as demonstrated by its application to image and video datasets. The project also includes a PixelCNN prior trained on the discrete latents for class-conditional generation.

Quick Start & Requirements

• Install/Run: python vqvae.py --data-folder /tmp/miniimagenet --output-folder models/vqvae
• Prerequisites: PyTorch, Python 3.x. Tested with MNIST, FashionMNIST, CIFAR10, Mini-ImageNet, and Atari 2600 (Boxing).
• Links: final_project.pdf

Highlighted Details

• Custom PyTorch VectorQuantization implementation reportedly speeds up VQ-VAE training by ~3x.
• Includes code for VQ-VAE reconstructions and class-conditional samples using a PixelCNN prior.
• Basic tests for vector quantization functions are included.

Maintenance & Community

Authors include Rithesh Kumar, Tristan Deleu, and Evan Racah. No community links or roadmap information are provided in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not addressed.

Limitations & Caveats

The project appears to be a course project reproduction, and its current maintenance status or long-term support is unclear. No explicit limitations or known issues are mentioned.