June 18, 2018
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone.
Research Topics
November 10, 2022
Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado
November 10, 2022
November 06, 2022
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
November 06, 2022
October 25, 2022
Mustafa Mukadam, Austin Wang, Brandon Amos, Daniel DeTone, Jing Dong, Joe Ortiz, Luis Pineda, Maurizio Monge, Ricky Chen, Shobha Venkataraman, Stuart Anderson, Taosha Fan, Paloma Sodhi
October 25, 2022
October 22, 2022
Naila Murray, Lei Wang, Piotr Koniusz, Shan Zhang
October 22, 2022
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Eliya Nachmani, Lior Wolf
December 11, 2019
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
Yedid Hoshen, Lior Wolf
November 01, 2018
Foundational models
Our approach
Latest news
Foundational models