Deep Fake Challenge by Facebook team

#Facebook launches a competition to fight deep fakes. Unfortunately, results of this competition will be obviously used to create better fakes, to the cheers of the people, wishing to watch the Matrix with Bruce Lee or more questionable deep fake applications.

Link: https://ai.facebook.com/blog/deepfake-detection-challenge/

#deepfake #video #cv #dl

Video on how Facebook continues to develop its #Portal device

How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.

Link: https://ai.facebook.com/blog/smart-camera-portal-advances/

#CV #DL #Segmentation

​​Online speech recognition with wav2letter@anywhere

Facebook have open-sourced wav2letter@anywhere, an inference framework for online speech recognition that delivers state-of-the-art performance.

Link: https://ai.facebook.com/blog/online-speech-recognition-with-wav2letteranywhere/

#wav2letter #audiolearning #soundlearning #sound #acoustic #audio #facebook

​​HiPlot: High-dimensional interactive plots made easy

Interactive parameters' performance #visualization tool. This new Facebook AI's release enables researchers to more easily evaluate the influence of their hyperparameters, such as learning rate, regularizations, and architecture.

Link: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy
Github: https://github.com/facebookresearch/hiplot
Demo: https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html
Pip: pip install hiplot

#hyperopt #facebook #opensource

​​Transferring Dense Pose to Proximal Animal Classes

Article on how to train DensePose for animals withiout labels

DensePose approach predicts the pose of humans densely and accurately given a large dataset of poses annotated in detail. It's super expensive to collect DensePose annotations for all different classes of animals. So authors show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in DensePose for humans. They propose to utilize the existing annotations of humans and do self-training on unlabeled images of animals.

Link: https://asanakoy.github.io/densepose-evolution/
YouTube: https://youtu.be/OU3Ayg_l4QM
Paper: https://arxiv.org/pdf/2003.00080.pdf

#Facebook #FAIR #CVPR #CVPR2020 #posetransfer #dl

🤖 The NetHack Learning Environment


#Facebook launched new Reinforcement Learning environment for training agents based on #NetHack game. Nethack has nothing to do with what is considered common cybersecurity now, but it is an early terminal-based Minecraft (as a matter of fact one might say «console NetHack game» to go ‘all in’ in a word pun game).

NetHack is a wonderful RPG adventure game, happening in dungeon. Players control @ sign moving in ASCII-based environment, fighting enemies and doing quests. If you haven’t played it you are missing a whole piece of gaming culture and our editorial team kindly cheers you on at least trying to play it. Though now there lots of wikis and playing guides, canonicial way to play it is to dive into source code for looking up the keys and getting the whole idea of what to expect from different situations.

#NLE uses python and ZeroMQ and we are looking forward to interesting applications or showcases to arise from this release.

Github: https://github.com/facebookresearch/nle
NetHack official page: http://nethack.org

#RL

Update from #Facebook on #Nethack learning Environment.

Link: https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning
Publication: https://arxiv.org/abs/2006.13760

#RL

​​Making Animoji or any other 3D avatar more human-like

#Facebook researchers suggested an approach for more precise facial and gaze expression tracking.

Review: https://syncedreview.com/2020/07/07/facebook-introduces-integrated-eye-face-model-for-3d-immersion-in-remote-communication/
Paper: https://research.fb.com/wp-content/uploads/2020/06/The-Eyes-Have-It-An-Integrated-Eye-and-Face-Model-for-Photorealistic-Facial-Animation.pdf

#eyetracking #cv #dl #3davatar #videolearning #facerecognition

​​A new SOTA on voice separation model that distinguishes multiple speakers simultaneously

Pandemic given a sufficient rise to new technologies covering voice communication. Noise cancelling is required more than ever and now #Facebook introduced a new method for separating as many as five voices speaking simultaneously into a single microphone. It pushes state of the art on multiple benchmarks, including ones with challenging noise and reverberations.

Blogpost: https://ai.facebook.com/blog/a-new-state-of-the-art-voice-separation-model-that-distinguishes-multiple-speakers-simultaneously
Paper: https://arxiv.org/pdf/2003.01531.pdf

#SOTA #FacebookAI #voicerecognition #soundlearning #DL

#Facebook released github repo with code for #TransCoder : https://github.com/facebookresearch/TransCoder/

SEER: The start of a more powerful, flexible, and accessible era for computer vision

#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.

And here it is — model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.

Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)

Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf

#facebook #fair #cv #dl

Habitat 2.0: Training home assistant robots with faster simulation and new benchmarks

Facebook released a new simulation platform to train robots in. Yeah, virtual robots in virtual environment, which can be a real space replica. This work brings us closer to domestic use of assistive robots.

Project website: https://ai.facebook.com/blog/habitat-20-training-home-assistant-robots-with-faster-simulation-and-new-benchmarks
Paper: https://ai.facebook.com/research/publications/habitat-2.0-training-home-assistants-to-rearrange-their-habitat

#Facebook #DigitalTwin #VR #RL #assistiverobots

🦜 Hi!

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Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.


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