924 citations · 4.2k across the 19 of their papers we have counts for
4 papers · 1 filter
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Junnan Li, Caiming Xiong, Steven Hoi
Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning met…
MoPro: Webly Supervised Learning with Momentum Prototypes
Junnan Li, Caiming Xiong, Steven C. H. Hoi
We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of sel…
Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong +1
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive…
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li, Richard Socher, Steven C. H. Hoi
Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent direction…