2020: 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

Even with everything that happened in the world this year, we still had the chance to see a lot of amazing research come out. Especially in the field of artificial intelligence. More, many important aspects were highlighted this year, like the ethical aspects, important biases, and much more. Artificial intelligence and our understanding of the human brain and its link to AI is constantly evolving, showing promising applications in the soon future.

Here are the most interesting research papers of the year, in case you missed any of them. In short, it is basically a 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.

Maintainer - louisfb01

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🆕 Check the 2021 repo!

Feel free to message me any great papers I missed to add to this repository on bouchard.lf@gmail.com

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

Watch a complete 2020 rewind in 15 minutes

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If you are interested in computer vision research, here is another great repository for you:

The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.

Top 10 Computer Vision Papers 2020

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The Full List

• YOLOv4: Optimal Speed and Accuracy of Object Detection [1]
• DeepFaceDrawing: Deep Generation of Face Images from Sketches [2]
• Learning to Simulate Dynamic Environments with GameGAN [3]
• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [4]
• Unsupervised Translation of Programming Languages [5]
• PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization [6]
• High-Resolution Neural Face Swapping for Visual Effects [7]
• Swapping Autoencoder for Deep Image Manipulation [8]
• GPT-3: Language Models are Few-Shot Learners [9]
• [Learning Joint Spatial-Temporal T