{"data":{"slug":"louisfb01-best-ai-papers-2021","name":"best_AI_papers_2021","tagline":"A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.","github_url":"https://github.com/louisfb01/best_AI_papers_2021","owner":"louisfb01","repo":"best_AI_papers_2021","owner_avatar_url":"https://avatars.githubusercontent.com/u/70274208?v=4","primary_language":null,"stars":2897,"forks":238,"topics":["2021","ai","artificial-intelligence","artificialintelligence","computer-science","computer-vision","deep-learning","innovation","machine-learning","machinelearning","paper","papers","python","research","research-paper","sota","sota-technique","state-of-art","state-of-the-art","technology"],"archived":false,"github_pushed_at":"2023-10-18T11:46:35+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/louisfb01-best-ai-papers-2021","markdown_url":"https://www.graphcanon.com/tools/louisfb01-best-ai-papers-2021.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/louisfb01-best-ai-papers-2021","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=louisfb01-best-ai-papers-2021","description":"A  curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.","homepage_url":"https://www.louisbouchard.ai/2021-ai-papers-review/","license":"MIT","open_issues":0,"watchers":83,"ai_summary":null,"readme_excerpt":"# 2021: A Year Full of Amazing AI papers- A Review 📌\n## 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.\n\nWhile 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.\n\n>\"Science cannot tell us what we ought to do, only what we can do.\"<br/>- Jean-Paul Sartre, Being and Nothingness\n\nHere 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!\n\n**The complete reference to each paper is listed at the end of this repository.** *Star this repository to stay up to date!* ⭐️\n\nMaintainer: [louisfb01](https://github.com/louisfb01)\n\n\n\nSubscribe to my [newsletter](https://louisbouchard.substack.com/) - The latest updates in AI explained every week.\n\n\n*Feel free to [message me](https://www.louisbouchard.ai/contact/) any interesting paper I may have missed to add to this repository.*\n\n*Tag me on **Twitter** [@Whats_AI](https://twitter.com/Whats_AI) or **LinkedIn** [@Louis (What's AI) Bouchard](https://www.linkedin.com/in/whats-ai/) if you share the list!*\n\n ### Watch a complete 2021 rewind in 15 minutes\n\n[<img src=\"https://imgur.com/3OoNOg1.png\" width=\"512\"/>](https://youtu.be/z5slE_akZmc)\n\n--- \n\n### If you are interested in Computer Vision research, here is another great repository for you:\nA curated list of the top 10 CV publications in 2021 with a clear video explanation, link to a more in-depth article, and code.\n\n[The Top 10 Computer Vision Papers of 2021](https://github.com/louisfb01/top-10-cv-papers-2021)\n\n----\n\n👀 **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](https://colab.research.google.com/github/louisfb01/examples/blob/master/colabs/pytorch/Simple_PyTorch_Integration.ipynb)! Since most of the code here is PyTorch-based, we thought that a [QuickStart guide](https://colab.research.google.com/github/louisfb01/examples/blob/master/colabs/pytorch/Simple_PyTorch_Integration.ipynb) for using W&B on PyTorch would be most interesting to share.\n\n👉Follow [this quick guide](https://colab.research.google.com/github/louisfb01/examples/blob/master/colabs/pytorch/Simple_PyTorch_Integration.ipynb), 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](https://colab.research.google.com/github/louisfb01/examples/blob/master/colabs/pytorch/Organizing_Hyperparameter_Sweeps_in_PyTorch_with_W%26B.ipynb) for using Hyperparameter Sweeps if interested :)\n\n🙌 Thank you to [Weights & Biases](https://wandb.ai/) for sponsoring this repository and the work I've been doing, and thanks to any of you using this link and trying W&B!\n\n\n\n----\n\n## The Full List\n- [DALL·E: Zero-Shot Text-to-Image Generation from OpenAI [1]](#1)\n- [VOGUE: Try-On by StyleGAN Interpolation Optimization [2]](#2)\n- [Taming Transformers for High-Resolution Image Synthesis [3]](#3)\n- [Thinking Fast And Slow in AI [4]](#4)\n- [Automatic detection and quantification of floating marine macro-litter in aerial images [5]](#5)\n- [ShaRF: Shape-conditioned Radiance Fields","github_created_at":"2021-11-06T16:55:30+00:00","created_at":"2026-07-11T12:23:13.887416+00:00","updated_at":"2026-07-11T12:23:29.41168+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"},{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"}],"tags":[{"slug":"computer-science","name":"computer-science"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"ai","name":"ai"},{"slug":"artificialintelligence","name":"artificialintelligence"},{"slug":"artificial-intelligence","name":"artificial-intelligence"},{"slug":"2021","name":"2021"},{"slug":"innovation","name":"innovation"},{"slug":"computer-vision","name":"computer-vision"}],"trust":{"provenance":{"is_fork":false,"github_id":425298533,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:23:14.529Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":997,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:23:17.599Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:23:17.284Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-11T12:23:17.284Z"}}}}