This repository provides a curated list of significant AI research papers from 2021, categorized by release date. It aims to inform AI researchers, practitioners, and enthusiasts about key breakthroughs, offering links to video explanations, in-depth articles, and code repositories for each paper.

How It Works

The repository acts as a structured index to the rapidly evolving AI landscape of 2021. Each entry links to external resources that break down complex research into digestible formats, facilitating understanding and adoption of new techniques. The curation focuses on papers with clear explanations and available code, promoting practical engagement with AI advancements.

Quick Start & Requirements

This is a curated list, not a software package. No installation is required. Users can browse the README for links to papers, explanations, and code.

Highlighted Details

• Covers a wide range of AI subfields including image generation (DALL-E, StyleGAN), computer vision (Swin Transformer, NeRF), natural language processing (GitHub Copilot), and audio processing.
• Includes papers with practical applications like deepfake detection, image restoration, and 3D model generation.
• Features links to video explanations and Google Colab demos for many entries, enhancing accessibility.
• Highlights award-winning papers (e.g., CVPR 2021 Best Paper) and contributions from major research institutions and companies (OpenAI, Google, NVIDIA, Apple, Meta).

Maintenance & Community

The repository is maintained by louisfb01, who encourages community contributions for missed papers. Links to the maintainer's newsletter and social media (Twitter, LinkedIn) are provided for updates and engagement.

Licensing & Compatibility

The repository itself is not software and thus not subject to software licensing. Individual papers and their associated code repositories will have their own licenses, which users must adhere to.

Limitations & Caveats

The list is specific to 2021 and may not reflect the latest advancements beyond that year. While code is often linked, its integration or execution is the user's responsibility and may require specific environments or hardware.