Research

We're advancing the state-of-the-art in artificial intelligence through fundamental and applied research in open collaboration with the community.

Notable Papers

COMPUTER VISION

Live Face De-Identification in Video

Oran Gafni

Lior Wolf

Yaniv Taigman

International Conference on Computer Vision (ICCV)

RESEARCH

COMPUTER VISION

TensorMask: A Foundation for Dense Object Segmentation

Xinlei Chen

Ross Girshick

Kaiming He

Piotr Dollar

International Conference on Computer Vision (ICCV)

RESEARCH

Single-Network Whole-Body Pose Estimation

Gines Hidalgo

Yaadhav Raaj

Haroon Idrees

Donglai Xiang...

International Conference on Computer Vision (ICCV)

COMPUTER VISION

A Universal Music Translation Network

Noam Mor

Lior Wolf

Adam Polyak

Yaniv Taigman

International Conference on Learning Representations (ICLR)

Recent Publications

April 27, 2020

RESEARCH

The Early Phase of Neural Network Training

Recent studies have shown that many important aspects of neural network learning take place within the very earliest iterations or epochs of training. For example, sparse, trainable sub-networks emerge (Frankle et al., 2019), gradient descent…

Jonathan Frankle, David J. Schwab, Ari Morcos

April 27, 2020

April 26, 2020

RESEARCH

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local…

Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael Rabbat

April 26, 2020

April 25, 2020

RESEARCH

And the bit goes down: Revisiting the quantization of neural networks

In this paper, we address the problem of reducing the memory footprint of convolutional network architectures. We introduce a vector quantization method that aims at preserving the quality of the reconstruction of the network outputs rather…

Pierre Stock, Armand Joulin, Rémi Gribonval, Benjamin Graham, Hervé Jégou

April 25, 2020

April 25, 2020

RESEARCH

Permutation Equivariant Models for Compositional Generalization in Language

Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of…

Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt

April 25, 2020

Fundamental & Applied Research

At Facebook AI, we conduct both fundamental and applied research to advance our understanding and impact product experiences. We publish our discoveries in peer reviewed academic journals and conferences, and build AI technologies used by billions of people around the world.

Fundamental Research

FAIR seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.

Along with the key principles of Facebook AI - openness, collaboration, excellence, and scale - we believe FAIR researchers also need to have the freedom and autonomy to design and follow their own research agendas so they can take on the most impactful work and develop the most disruptive projects, all while sharing their results with the community.

Applied Research

Facebook AI Applied Research engages in cutting-edge research that can improve and power new product experiences at huge scale for our community. Building on Facebook AI's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together.

Our Values

We align our fundamental and applied research efforts and applications around a few key principles:

Openness

We believe the latest advancements in AI should be published and open-sourced for the community to learn about and build upon.

Collaboration

We collaborate openly with both internal and external partners to share knowledge and cultivate diverse perspectives and needs.

Excellence

There is no shortage of new areas to explore in AI - our researchers focus on the projects that we believe will have the most positive impact on people and society.

Scale

To bring the benefits of AI to more people and improve accessibility, our research must account for both large scale data and computation needs.

Help Us Pioneer the Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.