avelino /
awesome-go
A curated list of awesome Go frameworks, libraries and software
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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
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!
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.
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.
GameGAN, a generative adversarial network trained on 50,000 PAC-MAN episodes, produces a fully functional version of the dot-munching classic without an underlying game engine.
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.
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.
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!
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.
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!
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.
Selected from shared topics, language and repository description—not editorial ratings.
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