🔥Singing voice conversion system developed at FAIR-Tel Aviv.

This can transform someone's singing voice into someone else's voice.

YouTube: https://www.youtube.com/watch?v=IEpkGenLnjw
Link: https://venturebeat.com/2019/04/16/facebooks-ai-can-convert-one-singers-voice-into-another/
ArXiV: https://arxiv.org/abs/1904.06590

#voiceconversion #audiolearning #DL #Facebook

All videos & slides from Scaled Machine Learning Conference 2019

YouTube playlist: https://www.youtube.com/playlist?list=PLRM2gQVaW_wWXoUnSfZTxpgDmNaAS1RtG

#Facebook #dl #ScaledML2019

New deep learning framework from Facebook

Pythia is a deep learning framework that supports multitasking in the vision and language domain. Built on our open-source #PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. #Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions.

Link: https://code.fb.com/ai-research/pythia/
GitHub: https://github.com/facebookresearch/pythia

#Facebook #FacebookAI #DL #CV #multimodal

​​Accelerating MRI reconstruction via active acquisition

Researchers from #Facebook AI propose a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.

Link: https://ai.facebook.com/blog/accelerating-mri-reconstruction/
Paper link: https://research.fb.com/publications/reducing-uncertainty-in-undersampled-mri-reconstruction-with-active-acquisition/

#CV #DL #CVPR2019 #healthcare #MRI #biolearning

Intro to Pythia — Visual Question Answering framework from Facebook

Pythia works in terms of #VQA by taking input picture and question and providing the answer to the latter in terms of picture semantics.


Link: https://link.medium.com/dknDKSuVqX
Previously: https://yangx.top/opendatascience/812

#DL #facebook #pythia #VQA #opensource

​​💣New open-source recommender system from Facebook.

Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.

Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109

#Facebook #DLRM #recommender #DL #PyTorch #Caffe

Facebook open sourced video alignment algorithms that detect identical and near identical videos to build more robust defenses against harmful visual content.

Project page: https://newsroom.fb.com/news/2019/08/open-source-photo-video-matching/
Code: https://github.com/facebookresearch/videoalignment

#Facebook #video #cv #dl

​​Long-form question answering

Facebook AI shared the first large-scale data set, code, and baseline models for long-form QA, which requires machines to provide long, complex answers — something that existing algorithms have not been challenged to do before.

Link: https://ai.facebook.com/blog/longform-qa/

#FacebookAI #Facebook #NLP #NLU #QA

Open-sourcing hyperparameter autotuning for fastText

Facebook AI researchers are releasing a new feature for the fastText library which provides hyper-parameter autotuning for more efficient text classifiers.

Link: https://ai.facebook.com/blog/fasttext-blog-post-open-source-in-brief/

#FacebookAI #Facebook #FastText #NLU #NLP

​​New fastMRI challenge from #FacebookAI team

Submission deadline: September 19

Announcement link: https://ai.facebook.com/blog/fastmri-challenge/
Competition link: https://fastmri.org/

#Competition #NotOnlyKaggle #Facebook #CV #DL

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