This repository serves as a curated collection of research papers, blog posts, and resources focused on Vector Quantized Variational Autoencoders (VQ-VAEs) and their diverse applications. It targets researchers and practitioners in machine learning, computer vision, and generative modeling seeking to understand and implement VQ-VAE architectures. The primary benefit is a centralized, up-to-date overview of the VQ-VAE landscape, facilitating exploration of state-of-the-art techniques.

How It Works

The repository organizes papers and resources by application domain, including image generation, video synthesis, 3D shape modeling, and speech/audio processing. It highlights key papers that introduce novel VQ-VAE variants, improvements, or significant applications, often linking to official publications, code repositories, or explanatory blog posts. This structured approach allows users to quickly identify relevant advancements and foundational work within specific subfields.

Quick Start & Requirements

This repository is a collection of links and does not require installation or execution. All listed papers and resources are external.

Highlighted Details

• Comprehensive coverage of VQ-VAE advancements from foundational papers (e.g., VQ-VAE-2, Neural Discrete Representation Learning) to recent applications in text-to-image, text-to-video, and multimodal generation.
• Includes resources on VQ-VAE variants like VQGAN, BEiT, and SoundStream, showcasing their impact across various domains.
• Features links to explanatory blog posts that demystify complex concepts like DALL-E's VQ-VAE components.
• Organizes content by application area, providing a clear path for users interested in specific VQ-VAE use cases.

Maintenance & Community

The repository is maintained by rese1f. The last update appears to be around February 2021 for blog posts, with papers listed up to arXiv 2024 and CVPR 2023. There are no explicit community links (e.g., Discord, Slack) provided.

Licensing & Compatibility

The repository itself is licensed under an unspecified license. The linked papers are subject to their respective publication and copyright terms. Compatibility for commercial use depends on the licenses of the individual linked projects and papers.

Limitations & Caveats

This is a curated list of resources, not an executable library. It does not provide code implementations directly, requiring users to seek out associated repositories for practical application. The recency of blog posts may lag behind the latest research papers.