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

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

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Watch a complete 2021 rewind in 15 minutes

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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

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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 from a Single View [6]
• Generative Adversarial Transformers [7]
• We Asked Artificial Intelligence to Create Dating Profiles. Would You Swipe Right? [8]
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [9]
• IMAGE GANS MEET DIFFERENTIABLE RENDERING FOR INVERSE GRAPHICS AND INTERPRETABLE 3D NEURAL RENDERING [10]
• Deep nets: What have they ever done for vision? [11]
• Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image [12]
• Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control [13]
• Total Relighting: Learning to Relight Portraits for Background Replacement [14]
• LASR: Learning Articulated Shape Reconstruction from a Monocular Video [15]
• Enhancing Photorealism Enhancement [16]
• DefakeHop: A Light-Weight High-Performance Deepfake Detector [17]
• High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network [18]
• Barbershop: GAN-based Image Compositing using Segmentation Masks [19]
• TextStyleBrush: Transfer of text aesthetics from a single example [20]

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DALL·E: Zero-Shot Text-to-Image Generation from OpenAI [1]

OpenAI successfully trained a network able to generate images from text captions. It is very similar to GPT-3 and Image GPT and produces amazing results.

• Short Video Explanation:
• Short read: OpenAI’s DALL·E: Text-to-Image Generation Explained
• Paper: Zero-Shot Text-to-Image Generation
• Code: Code & more information for the discrete VAE used for DALL·E

VOGUE: Try-On by StyleGAN Interpolation Optimization [2]

Google used a modified StyleGAN2 architecture to create an online fitting room where you can automatically try-on any pants or shirts you want using only an image of yourself.

• Short Video Explanation:
• Short read: The AI-Powered Online Fitting Room: VOGUE
• Paper: VOGUE: Try-On by StyleGAN Interpolation Optimization

Taming Transformers for High-Resolution Image Synthesis [3]

Tl;DR: They combined the efficiency of GANs and convolutional approaches with the expressivity of transformers to produce a powerful and time-efficient method for semantically-guided high-quality image synthesis.

• Short Video Explanation:
• Short read: Combining the Transformers Expressivity with the CNNs Efficiency for High-Resolution Image Synthesis
• Paper: Taming Transformers for High-Resolution Image Synthesis
• Code: Taming Transformers

Thinking Fast And Slow in AI [4]

Drawing inspiration from Human Capabilities Towards a more general and trustworthy AI & 10 Questions for the AI Research Community.

• Short Video Explanation:
• Short read: Third Wave of AI | Thinking Fast and Slow
• Paper: Thinking Fast And Slow in AI

Automatic detection and quantification of floating marine macro-litter in aerial images [5]

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify these garbages, called floating marine macro-litter, or FMML, within images of the sea surface.

• Short Video Explanation:
• Short read: An AI Software Able To Detect and Count Plastic Waste in the Ocean
• Paper: Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R, Environmental Pollution
• Click here for the code

ShaRF: Shape-conditioned Radiance Fields from a Single View [6]

Just imagine how cool it would be to just take a picture of an object and have it in 3D to insert in the movie or video game you are creating or in a 3D scene for an illustration.

• Short Video Explanation:
• Short read: ShaRF: Take a Picture From a Real-Life Object, and Create a 3D Model of It
• Paper: ShaRF: Shape-conditioned Radiance Fields from a Single View
• Click here for the code

Generative Adversarial Transformers [7]

They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!

• Short Video Explanation:
• Short read: GANsformers: Scene Generation with Generative Adversarial Transformers
• Paper: [Generative Adversarial Transformers](https://a