GitHub - facebookresearch/vissl: VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
PyText: A seamless path from NLP research to production – Facebook Research
We introduce PyText1 – a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine
AugLy: A new data augmentation library to help build more robust AI models
AugLy: A new data augmentation library to help build more robust AI models What the research is:We are open-sourcing AugLy, a new Python library that will help AI researchers use data augmentations to evaluate and improve the robustness of their machine learning models. Augmentations can include a wide variety of modifications to a piece of content, ranging from recropping a photo to changing the
DeSIGN: Design Inspiration from Generative Networks
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from no
Powered by AI: Advancing product understanding and building new shopping experiences
Powered by AI: Advancing product understanding and building new shopping experiences Today we’re announcing: We’ve built and deployed GrokNet, a universal computer vision system designed for shopping. It can identify fine-grained product attributes across billions of photos — in different categories, such as fashion, auto, and home decor. GrokNet is powering new Marketplace features for buyers and
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications - Meta Research | Meta Research
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware ImplicationsarXiv The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper we provide detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of o
カネを稼がない技術は積極公開、フェイスブックのしたたかな戦略
オンプレミスでディープラーニング（深層学習）を活用するユーザーにとって、米フェイスブック（Facebook）は最も頼れる存在になるかもしれない。深層学習フレームワークだけでなく、学習済みモデルもオープンソースソフトウエア（OSS）として公開し始めたからだ。AI（人工知能）クラウドで稼ぎたい競合には追随できない手法で自社製OSSの普及を図る。 フェイスブックは2018年5月1、2日に米サンノゼで開催した開発者会議「Facebook F8」で、AIに関する新しい施策を発表した。2日目の基調講演に登壇したCTO（最高技術責任者）のマイク・シュローファー（Mike Schroepfer）氏は、同社が中心となって開発を進めるOSSの深層学習フレームワーク「Caffe2」と「PyTorch」を統合して数カ月以内に「PyTorch 1.0」としてリリースする計画や、フェイスブックによる学習済みの機械学習
Mask R-CNN
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognit
Facebook to open-source AI hardware design
FAIR has achieved noted advancements in the development of AI training hardware considered to be among the best in the world. We have done this through a combination of hardware expertise, partner relationships with vendors, and a significant strategic investment in AI research. FAIR is more than tripling its investment in GPU hardware as we focus even more on research and enable other teams acros
Facebook’s open-sourcing of AI hardware is the start of the deep-learning revolution
A few days ago, Facebook open-sourced its artificial intelligence (AI) hardware computing design. Most people don’t know that large companies such as Facebook, Google, and Amazon don’t buy hardware from the usual large computer suppliers like Dell, HP, and IBM but instead design their own hardware based on commodity components. The Facebook website and all its myriad apps and subsystems persist on
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GitHub - facebookresearch/Detectron: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Off-Line, Off-Policy RL for Real-World Decision Making at Facebook with Jason Gauci | The TWIML AI Podcast
Yuxiao Dong's Homepage
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http://www.sysml.cc/doc/2019/30.pdf
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“Predictive Learning” is the New Buzzword in Deep Learning
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