Home/Computer Vision/best_AI_papers_2021
best_AI_papers_2021 logo

best_AI_papers_2021

louisfb01/best_AI_papers_2021

A curated list of AI research papers from 2021 with explanations and resources

GraphCanon updated 2w · GitHub synced 2w · 25 views this month

2.9k stars237 forksLast push 2y MIT

Decision brief

Best_AI_papers_2021 offers a curated list of key AI papers published in 2021 with videos, articles, and code examples.

Good fit when

  • If you are seeking current insights into AI advancements from 2021, especially on topics such as ethical considerations or governance aspects.
  • For researchers interested in the latest developments in machine learning or deep learning practices covered in papers of that particular year.

Avoid when

  • Should not be used if one is looking for historical context beyond AI advances strictly from the period 2021, as it focuses specifically on that time frame.
  • Not recommended if comprehensive coverage of AI research topics outside the themes covered in papers published solely in 2021 are needed.
Hosting:
unknown - The code repository does not specify the primary programming language used for the content but contains links to Python-based code samples.

Observed Jul 17, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Dormant (1016d since push)
As of 2w
Provenance
Not a fork · Personal account
As of 2w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/louisfb01/best_AI_papers_2021

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Curated collection of key AI breakthroughs from 2021, accompanied by video summaries, in-depth articles, and code where available, covering a broad spectrum of AI topics including machine learning, deep learning, computer vision, ethics, biases, governance, and transparency.

Capability facts

No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).

Categories

Tags

README

2021: A Year Full of Amazing AI papers- A Review 📌

A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.

While the world is still recovering, research hasn't slowed its frenetic pace, especially in the field of artificial intelligence. More, many important aspects were highlighted this year, like the ethical aspects, important biases, governance, transparency and much more. Artificial intelligence and our understanding of the human brain and its link to AI are constantly evolving, showing promising applications improving our life's quality in the near future. Still, we ought to be careful with which technology we choose to apply.

"Science cannot tell us what we ought to do, only what we can do."
- Jean-Paul Sartre, Being and Nothingness

Here are the most interesting research papers of the year, in case you missed any of them. In short, it is curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read!

The complete reference to each paper is listed at the end of this repository. Star this repository to stay up to date! ⭐️

Maintainer: louisfb01

Subscribe to my newsletter - The latest updates in AI explained every week.

Feel free to message me any interesting paper I may have missed to add to this repository.

Tag me on Twitter @Whats_AI or LinkedIn @Louis (What's AI) Bouchard if you share the list!

Watch a complete 2021 rewind in 15 minutes

Badge image


If you are interested in Computer Vision research, here is another great repository for you:

A curated list of the top 10 CV publications in 2021 with a clear video explanation, link to a more in-depth article, and code.

The Top 10 Computer Vision Papers of 2021


👀 If you'd like to support my work and use W&B (for free) to track your ML experiments and make your work reproducible or collaborate with a team, you can try it out by following this guide! Since most of the code here is PyTorch-based, we thought that a QuickStart guide for using W&B on PyTorch would be most interesting to share.

👉Follow this quick guide, use the same W&B lines in your code or any of the repos below, and have all your experiments automatically tracked in your w&b account! It doesn't take more than 5 minutes to set up and will change your life as it did for me! Here's a more advanced guide for using Hyperparameter Sweeps if interested :)

🙌 Thank you to Weights & Biases for sponsoring this repository and the work I've been doing, and thanks to any of you using this link and trying W&B!


The Full List

  • DALL·E: Zero-Shot Text-to-Image Generation from OpenAI [1]
  • VOGUE: Try-On by StyleGAN Interpolation Optimization [2]
  • Taming Transformers for High-Resolution Image Synthesis [3]
  • Thinking Fast And Slow in AI [4]
  • Automatic detection and quantification of floating marine macro-litter in aerial images [5]
  • [ShaRF: Shape-conditioned Radiance Fields

For agents

This page has a .md twin and JSON over the API.

Was this helpful?

Anonymous feedback helps us improve pages and translations.