本文推荐整理的2020年人工智能最新突破的25篇论文，包含论文下载、视频说明、代码下载。

这是今年最精彩的人工智能方向的研究论文，本文帮您整理了。简而言之，这是一份精心策划人工智能和数据科学领域最新突破的论文清单，包含了视频说明、论文下载、代码下载等等，此外，每一篇论文的完整参考文献都在文末进行罗列。

维护者：[louisfb01]:(https://github.com/louisfb01)

电子邮件 [email protected]

完整论文清单

• [YOLOv4: Optimal Speed and Accuracy of Object Detection [1]]

• [DeepFaceDrawing: Deep Generation of Face Images from Sketches [2]]

• [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [3]]

• [Unsupervised Translation of Programming Languages [4]]

• [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization [5]]

• [High-Resolution Neural Face Swapping for Visual Effects [6]]

• [Swapping Autoencoder for Deep Image Manipulation [7]]

• [GPT-3: Language Models are Few-Shot Learners [8]]

• [Learning Joint Spatial-Temporal Transformations for Video Inpainting [9]]

• [Image GPT - Generative Pretraining from Pixels [10]]

• [Learning to Cartoonize Using White-box Cartoon Representations [11]]

• [FreezeG: Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs [12]]

• [Neural Re-Rendering of Humans from a Single Image [13]]

• [I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image [14]]:(#14)

• [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments [15]]

• [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow [16]]

• [Crowdsampling the Plenoptic Function [17]]

• [Old Photo Restoration via Deep Latent Space Translation [18]]

• [Neural circuit policies enabling auditable autonomy [19]]

• [Lifespan Age Transformation Synthesis [20]]

• [COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning [21]]

• [Stylized Neural Painting [22]]

• [Is a Green Screen Really Necessary for Real-Time Portrait Matting? [23]]

• [ADA: Training Generative Adversarial Networks with Limited Data [24]]

• [Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere [25]]

• [Paper references]

YOLOv4: Optimal Speed and Accuracy of Object Detection [1]

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.

• [The YOLOv4 algorithm | Introduction to You Only Look Once, Version 4 | Real Time Object Detection ]:(https://youtu.be/CtjZFkO5RPw) - 短视频说明

• [The YOLOv4 algorithm | Introduction to You Only Look Once, Version 4 | Real-Time Object Detection]:(https://medium.com/what-is-artificial-intelligence/the-yolov4-algorithm-introduction-to-you-only-look-once-version-4-real-time-object-detection-5fd8a608b0fa) - 简短阅读

• [YOLOv4: Optimal Speed and Accuracy of Object Detection]:(https://arxiv.org/abs/2004.10934) - 论文下载

• [Click here for the Yolo v4 code]:(https://github.com/AlexeyAB/darknet) - 代码下载

DeepFaceDrawing: Deep Generation of Face Images from Sketches [2]

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.

• [AI Generates Real Faces From Sketches! DeepFaceDrawing Overview | Image-to-image translation in 2020]:(https://youtu.be/djXdgCVB0oM) - 短视频说明

• [AI Generates Real Faces From Sketches!]:(https://medium.com/what-is-artificial-intelligence/ai-generates-real-faces-from-sketches-8ccbac5d2b2e) - 简短阅读

• [DeepFaceDrawing: Deep Generation of Face Images from Sketches]:(http://geometrylearning.com/paper/DeepFaceDrawing.pdf) - 论文下载

• [Click here for the DeepFaceDrawing code]:(https://github.com/IGLICT/DeepFaceDrawing-Jittor) - 代码下载

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [3]

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 AI makes blurry faces look 60 times sharper! Introduction to PULSE: photo upsampling]:(https://youtu.be/cgakyOI9r8M) - 短视频说明

• [This AI makes blurry faces look 60 times sharper]:(https://medium.com/what-is-artificial-intelligence/this-ai-makes-blurry-faces-look-60-times-sharper-7fcd3b820910) - 简短阅读

• [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models]:(https://arxiv.org/abs/2003.03808) - 论文下载

• [Click here for the PULSE code]:(https://github.com/adamian98/pulse) - 代码下载

Unsupervised Translation of Programming Languages [4]

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 translates code from a programming language to another | Facebook TransCoder Explained]:(https://youtu.be/u6kM2lkrGQk) - 短视频说明

• [This AI translates code from a programming language to another | Facebook TransCoder Explained]:(https://medium.com/what-is-artificial-intelligence/this-ai-translates-code-from-a-programming-language-to-another-facebook-transcoder-explained-3017d052f4fd) - 简短阅读

• [Unsupervised Translation of Programming Languages]:(https://arxiv.org/abs/2006.03511) - 论文下载

• [Click here for the Transcoder code]:(https://github.com/facebookresearch/TransCoder?utm_source=catalyzex.com) - 代码下载

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization [5]

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!

• [AI Generates 3D high-resolution reconstructions of people from 2D images | Introduction to PIFuHD]:(https://youtu.be/ajWtdm05-6g) - 短视频说明

• [AI Generates 3D high-resolution reconstructions of people from 2D images | Introduction to PIFuHD]:(https://medium.com/towards-artificial-intelligence/ai-generates-3d-high-resolution-reconstructions-of-people-from-2d-images-introduction-to-pifuhd-d4aa515a482a) - 简短阅读

• [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization]:(https://arxiv.org/pdf/2004.00452.pdf) - 论文下载

• [Click here for the PiFuHD code]:(https://github.com/facebookresearch/pifuhd) - 代码下载

High-Resolution Neural Face Swapping for Visual Effects [6]

Researchers at Disney developed a new High-Resolution Face Swapping algorithm for Visual Effects in 论文下载 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.

• [Disney's New High Resolution Face Swapping Algorithm | New 2020 Face Swap Technology Explained]:(https://youtu.be/EzyhA46DQWA) - 短视频说明

• [Disney's New High-Resolution Face Swapping Algorithm | New 2020 Face Swap Technology Explained]:(https://medium.com/what-is-artificial-intelligence/disneys-new-high-resolution-face-swapping-algorithm-new-2020-face-swap-technology-explained-da7dc8caa2f2) - 简短阅读

• [High-Resolution Neural Face Swapping for Visual Effects]:(https://studios.disneyresearch.com/2020/06/29/high-resolution-neural-face-swapping-for-visual-effects/) - 论文下载

Swapping Autoencoder for Deep Image Manipulation [7]

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!

• [Texture-Swapping AI beats GANs for Image Manipulation! New Technique: Swapping Autoencoder Explained]:(https://youtu.be/hPR4cRzQY0s) - 短视频说明

• [Texture-Swapping AI beats GANs for Image Manipulation!]:(https://medium.com/what-is-artificial-intelligence/texture-swapping-ai-beats-gans-for-image-manipulation-e05700782183) - 简短阅读

• [Swapping Autoencoder for Deep Image Manipulation]:(https://arxiv.org/abs/2007.00653) - 论文下载

• [Click here for the Swapping autoencoder code]:(https://github.com/rosinality/swapping-autoencoder-pytorch?utm_source=catalyzex.com) - 代码下载

GPT-3: Language Models are Few-Shot Learners [8]

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.

• [OpenAI's New Language Generator: GPT-3 | This AI Generates Code, Websites, Songs & More From Words]:(https://youtu.be/gDDnTZchKec) - 短视频说明

• [Can GPT-3 Really Help You and Your Company?]:(https://medium.com/towards-artificial-intelligence/can-gpt-3-really-help-you-and-your-company-84dac3c5b58a) - 简短阅读

• [Language Models are Few-Shot Learners]:(https://arxiv.org/pdf/2005.14165.pdf) - 论文下载

• [Click here for GPT-3's GitHub page]:(https://github.com/openai/gpt-3) - The GitHub

Learning Joint Spatial-Temporal Transformations for Video Inpainting [9]

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!

• [This AI Takes a Video and Fills the Missing Pixels Behind an Object ! Video Inpainting]:(https://youtu.be/MAxMYGoN5U0) - 短视频说明

• [This AI takes a video and fills the missing pixels behind an object!]:(https://medium.com/towards-artificial-intelligence/this-ai-takes-a-video-and-fills-the-missing-pixels-behind-an-object-video-inpainting-9be38e141f46) - 简短阅读

• [Learning Joint Spatial-Temporal Transformations for Video Inpainting]:(https://arxiv.org/abs/2007.10247) - 论文下载

• [Click here for this Video Inpainting code]:(https://github.com/researchmm/STTN?utm_source=catalyzex.com) - 代码下载

Image GPT - Generative Pretraining from Pixels [10]

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!

• [This AI Can Generate the Other Half of a Picture Using a GPT Model]:(https://youtu.be/FwXQ568_io0) - 短视频说明

• [This AI Can Generate the Other Half of a Picture Using a GPT Model]:(https://medium.com/towards-artificial-intelligence/this-ai-can-generate-the-pixels-of-half-of-a-picture-from-nothing-using-a-nlp-model-7d7ba14b5522) - 简短阅读

• [Image GPT - Generative Pretraining from Pixels]:(https://openai.com/blog/image-gpt/) - 论文下载

• [Click here for the OpenAI's Image GPT code]:(https://github.com/openai/image-gpt) - 代码下载

Learning to Cartoonize Using White-box Cartoon Representations [11]

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!

• [This AI can cartoonize any picture or video you feed it! Paper Introduction & Results examples]:(https://youtu.be/GZVsONq3qtg) - 短视频说明

• [This AI can cartoonize any picture or video you feed it! Paper Introduction & Results examples]:(https://medium.com/what-is-artificial-intelligence/this-ai-can-cartoonize-any-picture-or-video-you-feed-it-paper-introduction-results-examples-d7e400d8c3e8) - 简短阅读

• [Learning to Cartoonize Using White-box Cartoon Representations]:(https://systemerrorwang.github.io/White-box-Cartoonization/paper/06791.pdf) - 论文下载

• [Click here for the Cartoonize code]:(https://github.com/SystemErrorWang/White-box-Cartoonization) - 代码下载

FreezeG: Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs [12]

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.

• [This Face Generating Model Transfers Real Face Photographs Into Distinctive Cartoon Styles | FreezeG]:(https://youtu.be/RvPUVniQiuw) - 短视频说明

• [This Face Generating Model Transfers Real Face Photographs Into Distinctive Cartoon Styles]:(https://medium.com/what-is-artificial-intelligence/this-face-generating-model-transfers-real-face-photographs-into-distinctive-cartoon-styles-33dde907737a) - 简短阅读

• [Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs]:(https://arxiv.org/pdf/2002.10964.pdf) - 论文下载

• [Click here for the FreezeG code]:(https://github.com/sangwoomo/freezeD?utm_source=catalyzex.com) - 代码下载

Neural Re-Rendering of Humans from a Single Image [13]

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.

• [Transfer clothes between photos using AI. From a single image!]:(https://youtu.be/E7fGsSNKMc4) - 短视频说明

• [Transfer clothes between photos using AI. From a single image!]:(https://medium.com/dataseries/transfer-clothes-between-photos-using-ai-from-a-single-image-4430a291afd7) - 简短阅读

• [Neural Re-Rendering of Humans from a Single Image]:(http://gvv.mpi-inf.mpg.de/projects/NHRR/data/1415.pdf) - 论文下载

I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image [14]

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• [Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image! With Code Publicly Avaibable!]:(https://youtu.be/tDz2wTixcrI) - 短视频说明

• [Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image! With Code Publicly Avaibable!]:(https://medium.com/dataseries/accurate-3d-human-pose-and-mesh-estimation-from-a-single-rgb-image-with-code-publicly-avaibable-b7cc995bcf2a) - 简短阅读

• [I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image]:(https://www.catalyzex.com/paper/arxiv:2008.03713?fbclid=IwAR1pQGBhIwO4gW4mVZm1UEtyPLyZInsLZMyq3EoANaWxGO0CZ00Sj3ViM7I) - 论文下载

Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments [15]

Language-guided navigation is a widely studied field and a very complex one. Indeed, it may seem simple for a human to just walk through a house to get to your coffee that you left on your nightstand to the left of your bed. But it is a whole other story for an agent, which is an autonomous AI-driven system using deep learning to perform tasks.

• [Language-Guided Navigation in 3D Environment | Facebook AI Research (with code publicly available!)]:(https://youtu.be/Fw_RUlUjuN4) - 短视频说明

• [Language-Guided Navigation in a 3D Environment]:(https://medium.com/r/?url=https%3A%2F%2Fbecominghuman.ai%2Flanguage-guided-navigation-in-a-3d-environment-e3cf4102fb89) - 简短阅读

• [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments]:(https://arxiv.org/pdf/2004.02857.pdf) - 论文下载

• [Click here for the VLN-CE code]:(https://github.com/jacobkrantz/VLN-CE) - 代码下载

RAFT: Recurrent All-Pairs Field Transforms for Optical Flow [16]

ECCV 2020 Best Paper Award Goes to Princeton Team. They developed a new end-to-end trainable model for optical flow. Their method beats state-of-the-art architectures' accuracy across multiple datasets and is way more efficient. They even made 代码下载 available for everyone on their Github!

• [ECCV 2020 Best Paper Award | RAFT: A New Deep Network Architecture For Optical Flow | WITH CODE]:(https://youtu.be/OSEuYBwOSGI) - 短视频说明

• [ECCV 2020 Best Paper Award | A New Architecture For Optical Flow]:(https://medium.com/towards-artificial-intelligence/eccv-2020-best-paper-award-a-new-architecture-for-optical-flow-3298c8a40dc7) - 简短阅读

• [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow]:(https://arxiv.org/pdf/2003.12039.pdf) - 论文下载

• [Click here for the RAFT code]:(https://github.com/princeton-vl/RAFT) - 代码下载

Crowdsampling the Plenoptic Function [17]

Using tourists' public photos from the internet, they were able to reconstruct multiple viewpoints of a scene conserving the realistic shadows and lighting! This is a huge advancement of the state-of-the-art techniques for photorealistic scene rendering and their results are simply amazing.

• [Reconstruct photorealistic scenes from tourists public photos on the internet!]:(https://youtu.be/F_JqJNBvJ64) - 短视频说明

• [Reconstruct Photorealistic Scenes from Tourists' Public Photos on the Internet!]:(https://medium.com/towards-artificial-intelligence/reconstruct-photorealistic-scenes-from-tourists-public-photos-on-the-internet-bb9ad39c96f3) - 简短阅读

• [Crowdsampling the Plenoptic Function]:(https://research.cs.cornell.edu/crowdplenoptic/) - 论文下载

• [Click here for the Crowdsampling code]:(https://github.com/zhengqili/Crowdsampling-the-Plenoptic-Function) - 代码下载

Old Photo Restoration via Deep Latent Space Translation [18]

Imagine having the old, folded, and even torn pictures of your grandmother when she was 18 years old in high definition with zero artifacts. This is called old photo restoration and this paper just opened a whole new avenue to address this problem using a deep learning approach.

• [Old Photo Restoration Using Deep Learning | 2020 Novel Approach Explained & Results]:(https://youtu.be/QUmrIpl0afQ) - 短视频说明

• [Old Photo Restoration using Deep Learning]:(https://medium.com/towards-artificial-intelligence/old-photo-restoration-using-deep-learning-47d4ab1bdc4d) - 简短阅读

• [Old Photo Restoration via Deep Latent Space Translation]:(https://arxiv.org/pdf/2009.07047.pdf) - 论文下载

• [Click here for the Old Photo Restoration code]:(https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life?utm_source=catalyzex.com) - 代码下载

Neural circuit policies enabling auditable autonomy [19]

Researchers from IST Austria and MIT have successfully trained a self-driving car using a new artificial intelligence system based on the brains of tiny animals, such as threadworms. They achieved that with only a few neurons able to control the self-driving car, compared to the millions of neurons needed by the popular deep neural networks such as Inceptions, Resnets, or VGG. Their network was able to completely control a car using only 75 000 parameters, composed of 19 control neurons, rather than millions!

• [A new brain-inspired intelligent system can drive a car using only 19 control neurons!]:(https://youtu.be/wAa358pNDkQ) - 短视频说明

• [A New Brain-inspired Intelligent System Drives a Car Using Only 19 Control Neurons!]:(https://medium.com/towards-artificial-intelligence/a-new-brain-inspired-intelligent-system-drives-a-car-using-only-19-control-neurons-1ed127107db9) - 简短阅读

• [Neural circuit policies enabling auditable autonomy]:(https://www.nature.com/articles/s42256-020-00237-3.epdf?sharing_token=xHsXBg2SoR9l8XdbXeGSqtRgN0jAjWel9jnR3ZoTv0PbS_e49wmlSXvnXIRQ7wyir5MOFK7XBfQ8sxCtVjc7zD1lWeQB5kHoRr4BAmDEU0_1-UN5qHD5nXYVQyq5BrRV_tFa3_FZjs4LBHt-yebsG4eQcOnNsG4BenK3CmBRFLk%3D) - 论文下载

• [Click here for the NCP code]:(https://github.com/mlech26l/keras-ncp) - 代码下载

Lifespan Age Transformation Synthesis [20]

A team of researchers from Adobe Research developed a new technique for age transformation synthesis based on only one picture from the person. It can generate the lifespan pictures from any picture you sent it.

• [Lifespan Age Transformation Synthesis | Generate Younger & Older Versions of Yourself !]:(https://youtu.be/xA-3cWJ4Y9Q) - 短视频说明

• [Generate Younger & Older Versions of Yourself!]:(https://medium.com/towards-artificial-intelligence/generate-younger-older-versions-of-yourself-1a87f970f3da) - 简短阅读

• [Lifespan Age Transformation Synthesis]:(https://arxiv.org/pdf/2003.09764.pdf) - 论文下载

• [Click here for the Lifespan age transformation synthesis code]:(https://github.com/royorel/Lifespan_Age_Transformation_Synthesis) - 代码下载

DeOldify

DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed and is still getting updated by only one person Jason Antic. It is now the state of the art way to colorize black and white images, and everything is open-sourced, but we will get back to this in a bit.

• [This AI can Colorize your Black & White Photos with Full Photorealistic Renders! (DeOldify)]:(https://youtu.be/1EP_Lq04h4M) - 短视频说明

• [This AI can Colorize your Black & White Photos with Full Photorealistic Renders! (DeOldify)]:(https://medium.com/towards-artificial-intelligence/this-ai-can-colorize-your-black-white-photos-with-full-photorealistic-renders-deoldify-bf1eed5cb02a) - 简短阅读

• [Click here for the DeOldify code]:(https://github.com/jantic/DeOldify) - 代码下载

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning [21]

As the name states, it uses transformers to generate accurate text descriptions for each sequence of a video, using both the video and a general description of it as inputs.

• [Video to Text Description Using Deep Learning and Transformers | COOT]:(https://youtu.be/5TRp5SuEtoY) - 短视频说明

• [Video to Text Description Using Deep Learning and Transformers | COOT]:(https://medium.com/towards-artificial-intelligence/video-to-text-description-using-deep-learning-and-transformers-coot-e05b8d0db110) - 简短阅读

• [COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning]:(https://arxiv.org/pdf/2011.00597.pdf) - 论文下载

• [Click here for the COOT code]:(https://github.com/gingsi/coot-videotext) - 代码下载

Stylized Neural Painting [22]

This Image-to-Painting Translation method simulates a real painter on multiple styles using a novel approach that does not involve any GAN architecture, unlike all the current state-of-the-art approaches!

• [Image-to-Painting Translation With Style Transfer]:(https://youtu.be/dzJStceOaQs) - 短视频说明

• [Image-to-Painting Translation With Style Transfer]:(https://medium.com/towards-artificial-intelligence/image-to-painting-translation-with-style-transfer-508618596409) - 简短阅读

• [Stylized Neural Painting]:(https://arxiv.org/abs/2011.08114) - 论文下载

• [Click here for the Stylized Neural Painting code]:(https://github.com/jiupinjia/stylized-neural-painting) - 代码下载

Is a Green Screen Really Necessary for Real-Time Portrait Matting? [23]

Human matting is an extremely interesting task where the goal is to find any human in a picture and remove the background from it. It is really hard to achieve due to the complexity of the task, having to find the person or people with the perfect contour. In this post, I review the best techniques used over the years and a novel approach published on November 29th, 2020. Many techniques are using basic computer vision algorithms to achieve this task, such as the GrabCut algorithm, which is extremely fast, but not very precise.

• [High-Quality Background Removal Without Green Screens | State of the Art Approach Explained]:(https://youtu.be/rUo0wuVyefU) - 短视频说明

• [High-Quality Background Removal Without Green Screens]:(https://medium.com/datadriveninvestor/high-quality-background-removal-without-green-screens-8e61c69de63) - 简短阅读

• [Is a Green Screen Really Necessary for Real-Time Portrait Matting?]:(https://arxiv.org/pdf/2011.11961.pdf) - 论文下载

• [Click here for the MODNet code]:(https://github.com/ZHKKKe/MODNet) - 代码下载

ADA: Training Generative Adversarial Networks with Limited Data [24]

With this new training method developed by NVIDIA, you can train a powerful generative model with one-tenth of the images! Making possible many applications that do not have access to so many images!

• [GAN Training Breakthrough for Limited Data Applications & New NVIDIA Program! NVIDIA Research]:(https://youtu.be/9fVNtVr_luc) - 短视频说明

• [GAN Training Breakthrough for Limited Data Applications & New NVIDIA Program! NVIDIA Research]:(https://medium.com/towards-artificial-intelligence/gan-training-breakthrough-for-limited-data-applications-new-nvidia-program-nvidia-research-3652c4c172e6) - 简短阅读

• [Training Generative Adversarial Networks with Limited Data]:(https://arxiv.org/abs/2006.06676) - 论文下载

• [Click here for the ADA code]:(https://github.com/NVlabs/stylegan2-ada) - 代码下载

Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere [25]

With this new training method developed by NVIDIA, you can train a powerful generative model with one-tenth of the images! Making possible many applications that do not have access to so many images!

• [An AI Predicting Faster and More Accurate Weather Forecasts]:(https://youtu.be/C7dNU298A0A) - 短视频说明

• [AI is Predicting Faster and More Accurate Weather Forecasts]:(https://medium.com/towards-artificial-intelligence/ai-is-predicting-faster-and-more-accurate-weather-forecasts-5d99a1d9c4f) - 简短阅读

• [Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere]:(https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002109) - 论文下载

• [Click here for the weather forecasting code]:(https://github.com/jweyn/DLWP-CS) - 代码下载

论文参考

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[2] S.-Y. Chen, W. Su, L. Gao, S. Xia, and H. Fu, "DeepFaceDrawing: Deep generation of face images from sketches," ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH2020), vol. 39, no. 4, 72:1–72:16, 2020.

[3] S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, Pulse: Self-supervised photo upsampling via latent space exploration of generative models, 2020. arXiv:2003.03808 [cs.CV].

[4] M.-A. Lachaux, B. Roziere, L. Chanussot, and G. Lample, Unsupervised translation of programming languages, 2020. arXiv:2006.03511 [cs.CL].

[5] S. Saito, T. Simon, J. Saragih, and H. Joo, Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization, 2020. arXiv:2004.00452 [cs.CV].

[6] J. Naruniec, L. Helminger, C. Schroers, and R. Weber, "High-resolution neural face-swapping for visual effects," Computer Graphics Forum, vol. 39, pp. 173–184, Jul. 2020.doi:10.1111/cgf.14062.

[7] T. Park, J.-Y. Zhu, O. Wang, J. Lu, E. Shechtman, A. A. Efros, and R. Zhang,Swappingautoencoder for deep image manipulation, 2020. arXiv:2007.00653 [cs.CV].

[8] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P.Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S.Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei,"Language models are few-shot learners," 2020. arXiv:2005.14165 [cs.CL].

[9] Y. Zeng, J. Fu, and H. Chao, Learning joint spatial-temporal transformations for video in-painting, 2020. arXiv:2007.10247 [cs.CV].

[10] M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, "Generative pretraining from pixels," in Proceedings of the 37th International Conference on Machine Learning, H. D. III and A. Singh, Eds., ser. Proceedings of Machine Learning Research, vol. 119, Virtual: PMLR, 13–18 Jul 2020, pp. 1691–1703. [Online]. Available:http://proceedings.mlr.press/v119/chen20s.html.

[11] Xinrui Wang and Jinze Yu, "Learning to Cartoonize Using White-box Cartoon Representations.", IEEE Conference on Computer Vision and Pattern Recognition, June 2020.

[12] S. Mo, M. Cho, and J. Shin, Freeze the discriminator: A simple baseline for fine-tuning gans,2020. arXiv:2002.10964 [cs.CV].

[13] K. Sarkar, D. Mehta, W. Xu, V. Golyanik, and C. Theobalt, "Neural re-rendering of humans from a single image," in European Conference on Computer Vision (ECCV), 2020.

[14] G. Moon and K. M. Lee, "I2l-meshnet: Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single rgb image," in European Conference on ComputerVision (ECCV), 2020

[15] J. Krantz, E. Wijmans, A. Majumdar, D. Batra, and S. Lee, "Beyond the nav-graph: Vision-and-language navigation in continuous environments," 2020. arXiv:2004.02857 [cs.CV].

[16] Z. Teed and J. Deng, Raft: Recurrent all-pairs field transforms for optical flow, 2020. arXiv:2003.12039 [cs.CV].

[17] Z. Li, W. Xian, A. Davis, and N. Snavely, "Crowdsampling the plenoptic function," inProc.European Conference on Computer Vision (ECCV), 2020.

[18] Z. Wan, B. Zhang, D. Chen, P. Zhang, D. Chen, J. Liao, and F. Wen, Old photo restoration via deep latent space translation, 2020. arXiv:2009.07047 [cs.CV].

[19] Lechner, M., Hasani, R., Amini, A. et al. Neural circuit policies enabling auditable autonomy. Nat Mach Intell 2, 642–652 (2020). https://doi.org/10.1038/s42256-020-00237-3

[20] R. Or-El, S. Sengupta, O. Fried, E. Shechtman, and I. Kemelmacher-Shlizerman, "Lifespanage transformation synthesis," in Proceedings of the European Conference on Computer Vision(ECCV), 2020.

[21] S. Ging, M. Zolfaghari, H. Pirsiavash, and T. Brox, "Coot: Cooperative hierarchical trans-former for video-text representation learning," in Conference on Neural Information ProcessingSystems, 2020.

[22] Z. Zou, T. Shi, S. Qiu, Y. Yuan, and Z. Shi, Stylized neural painting, 2020. arXiv:2011.08114[cs.CV].

[23] Z. Ke, K. Li, Y. Zhou, Q. Wu, X. Mao, Q. Yan, and R. W. Lau, "Is a green screen really necessary for real-time portrait matting?" ArXiv, vol. abs/2011.11961, 2020.

[24] T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, Training generative adversarial networks with limited data, 2020. arXiv:2006.06676 [cs.CV].

[25] J. A. Weyn, D. R. Durran, and R. Caruana, “Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere”, Journal of Advances in Modeling Earth Systems, vol. 12, no. 9, Sep. 2020, issn: 1942–2466.doi:10.1029/2020ms002109

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