Cristina Ferrero Castaño
2020: A Year Full of Amazing AI Papers — A Review [Part One]
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
Louis (What´s AI) Bouchard
Dec 21, 2020
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).
The complete reference to each paper is listed at the end of this article.
Watch a complete 2020 rewind in 15 minutes
 YOLOv4: Optimal Speed and Accuracy of Object Detection
This 4th version has been recently introduced in April 2020 by Alexey Bochkovsky et al. in the paper “YOLOv4: Optimal Speed and Accuracy of Object Detection”. The main goal of this algorithm was to make a super-fast object detector with high quality in terms of accuracy
 DeepFaceDrawing: Deep Generation of Face Images from Sketches
You can now generate high-quality face images from rough or even incomplete sketches with zero drawing skills using this new image-to-image translation technique! If your drawing skills as bad as mine you can even adjust how much the eyes, mouth, and nose will affect the final image! Let’s see if it really works and how they did it.
 Learning to Simulate Dynamic Environments with GameGAN
 PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
This new algorithm transforms a blurry image into a high-resolution image!
It can take a super low-resolution 16x16 image and turn it into a 1080p high definition human face! You don’t believe me? Then you can do just like me and try it on yourself in less than a minute! But first, let’s see how they did that.
 Unsupervised Translation of Programming Languages
This new model converts code from a programming language to another without any supervision! It can take a Python function and translate it into a C++ function, and vice-versa, without any prior examples! It understands the syntax of each language and can thus generalize to any programming language! Let’s see how they did that.
 PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
This AI Generates 3D high-resolution reconstructions of people from 2D images! It only needs a single image of you to generate a 3D avatar that looks just like you, even from the back!
 High-Resolution Neural Face Swapping for Visual Effects
Researchers at Disney developed a new High-Resolution Face Swapping algorithm for Visual Effects in the paper of the same name. It is capable of rendering photo-realistic results at megapixel resolution. Working for Disney, they are most certainly the best team for this work. Their goal is to swap the face of a target actor from a source actor while maintaining the actor’s performance. This is incredibly challenging and is useful in many circumstances, such as changing the age of a character, when an actor is not available, or even when it involves a stunt scene that would be too dangerous for the main actor to perform. The current approaches require a lot of frame-by-frame animation and post-processing by professionals.
 Swapping Autoencoder for Deep Image Manipulation
This new technique can change the texture of any picture while staying realistic using complete unsupervised training! The results look even better than what GANs can achieve while being way faster! It could even be used to create deepfakes!
 GPT-3: Language Models are Few-Shot Learners
The current state-of-the-art NLP systems struggle to generalize to work on different tasks. They need to be fine-tuned on datasets of thousands of examples while humans only need to see a few examples to perform a new language task. This was the goal behind GPT-3, to improve the task-agnostic characteristic of language models.
 Learning Joint Spatial-Temporal Transformations for Video Inpainting
This AI can fill the missing pixels behind a removed moving object and reconstruct the whole video with way more accuracy and less blurriness than current state-of-the-art approaches!
 Image GPT — Generative Pretraining from Pixels
A good AI, like the one used in Gmail, can generate coherent text and finish your phrase. This one uses the same principles in order to complete an image! All done in an unsupervised training with no labels required at all!
 Learning to Cartoonize Using White-box Cartoon Representations
This AI can cartoonize any picture or video you feed it in the cartoon style you want! Let’s see how it does that and some amazing examples. You can even try it yourself on the website they created as I did for myself!
 FreezeG: Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
This face generating model is able to transfer normal face photographs into distinctive styles such as Lee Mal-Nyeon’s cartoon style, the Simpsons, arts, and even dogs! The best thing about this new technique is that it’s super simple and significantly outperforms previous techniques used in GANs.
 Neural Re-Rendering of Humans from a Single Image
The algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image and easily reposed. Given an image of a person, they are able to create synthetic images of the person in different poses or with different clothing obtained from another input image.
In Part 2 we will continue to analyze the latest AI developments of the year.
To access the full article where you can find more detailed articles and the different codes...