179 results for "adversarial"
Publication
August 15, 2019
Xian Li, Juan Pino, Graham Neubig, Paul Michel
June 10, 2019
Marcus Rohrbach, Anna Rohrbach, Jae Sung Park, Trevor Darrell
May 24, 2019
Yedid Hoshen
April 30, 2019
Pascal Vincent, Hugo Bérard, Gauthier Gidel (lead author), Gaëtan Vignoud, Simon Lacoste-Julien
April 19, 2019
Y-Lan Boureau, Eric Smith, Guillaume Lample, Ludovic Denoyer, Marc'Aurelio Ranzato, Sandeep Subramanian
December 02, 2018
Jonas Gehring
September 12, 2018
Yedid Hoshen, Lior Wolf
September 10, 2018
Natalia Neverova, Iasonas Kokkinos, Riza Alp Guler
September 04, 2018
Anil Batra, Guan Pang, Manohar Paluri, Saikat Basu, Lorenzo Torresani, C.V. Jawahar, Suriya Singh
September 03, 2018
Pierre Stock, Moustapha Cisse
July 15, 2018
Alessandro Lazaric, Martin Riedmiller, Matteo Pirotta, Roberto Calandra, Sergey Levine
July 10, 2018
Piotr Bojanowski, Armand Joulin, Arthur Szlam, David Lopez-Paz
Blog
July 18, 2021
We’re sharing our work on few-shot neural architecture search (NAS), which combines the accuracy of vanilla NAS with the speed and efficiency of one-shot NAS. Few-shot NAS lets anyone design a powerful custom model quickly, with just a few GPUs.
June 30, 2021
Facebook AI is now open-sourcing our entire stack of COVID-19 forecasting models so that response teams, governments, and researchers can use them to further help their communities.
June 21, 2021
Facebook AI is contributing to ongoing work to identify manipulated images and improve the detection of data provenance with the Image Similarity data set and challenge, hosted by DrivenData and recently launched at CVPR 2021.
June 11, 2021
Today, we’re introducing TextStyleBrush, the first self-supervised AI model that replaces text in existing images of both scenes and handwriting — in one shot — using just a single example word.
June 02, 2021
Facebook is making PyTorch the default framework for building all our AI and machine learning models. Here’s how PyTorch is already powering the next generation of Facebook experiences, and what the future holds.
May 21, 2021
To enable speech recognition technology for many more languages spoken around the globe, Facebook AI is releasing wav2vec Unsupervised, a new method to train models with no supervision whatsoever. Wav2vec Unsupervised rivals the performance of the best supervised systems from just a few years ago.
May 24, 2021
We're introducing Dynaboard, an evaluation-as-a-service platform for conducting comprehensive evaluations of NLP models.
March 04, 2021
How can we build machines with human-level intelligence? There’s a limit to how far the field of AI can go with supervised learning alone. Here's why self-supervised learning is one of the most promising ways to make significant progress in AI.
December 05, 2018
Five years ago, we created the Facebook AI Research (FAIR) group to advance the state of the art of AI through open research for the benefit of all — it’s an effort to understand the nature of intelligence so that we might create intelligent machines.
June 12, 2020
2,114 participants around the globe entered the Deepfake Detection Challenge. We’re now sharing the winning models and insights from this first-of-its-kind open initiative to address the challenge of deepfake videos and images.
February 25, 2020
As part of the fastMRI initiative, Facebook AI and NYU Langone Health researchers have used a technique known as an orientation adversary to remove artifacts from AI-accelerated scans. This removes a major obstacle blocking deep learning use in MRIs.
December 11, 2019
We’ve launched the Deepfake Detection Challenge, an open, collaborative initiative to accelerate development of new technologies for detecting deepfakes and manipulated media.
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