Robust Contrastive Learning against Noisy Views
- URL: http://arxiv.org/abs/2201.04309v1
- Date: Wed, 12 Jan 2022 05:24:29 GMT
- Title: Robust Contrastive Learning against Noisy Views
- Authors: Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi,
Antonio Torralba, Stefanie Jegelka, Yale Song
- Abstract summary: We propose a new contrastive loss function that is robust against noisy views.
We show that our approach provides consistent improvements over the state-of-the-art image, video, and graph contrastive learning benchmarks.
- Score: 79.71880076439297
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Contrastive learning relies on an assumption that positive pairs contain
related views, e.g., patches of an image or co-occurring multimodal signals of
a video, that share certain underlying information about an instance. But what
if this assumption is violated? The literature suggests that contrastive
learning produces suboptimal representations in the presence of noisy views,
e.g., false positive pairs with no apparent shared information. In this work,
we propose a new contrastive loss function that is robust against noisy views.
We provide rigorous theoretical justifications by showing connections to robust
symmetric losses for noisy binary classification and by establishing a new
contrastive bound for mutual information maximization based on the Wasserstein
distance measure. The proposed loss is completely modality-agnostic and a
simple drop-in replacement for the InfoNCE loss, which makes it easy to apply
to existing contrastive frameworks. We show that our approach provides
consistent improvements over the state-of-the-art on image, video, and graph
contrastive learning benchmarks that exhibit a variety of real-world noise
patterns.
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