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Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
Ching-Yun (Irene) Ko · Jeet Mohapatra · Sijia Liu · Pin-Yu Chen · Luca Daniel · Lily Weng

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #608

As a seminal tool in self-supervised representation learning, contrastive learning has gained unprecedented attention in recent years. In essence, contrastive learning aims to leverage pairs of positive and negative samples for representation learning, which relates to exploiting neighborhood information in a feature space. By investigating the connection between contrastive learning and neighborhood component analysis (NCA), we provide a novel stochastic nearest neighbor viewpoint of contrastive learning and subsequently propose a series of contrastive losses that outperform the existing ones. Under our proposed framework, we show a new methodology to design integrated contrastive losses that could simultaneously achieve good accuracy and robustness on downstream tasks. With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.

Author Information

Ching-Yun (Irene) Ko (MIT)
Jeet Mohapatra (MIT)
Sijia Liu (Michigan State University)
Pin-Yu Chen (IBM Research AI)
Luca Daniel (Massachusetts Institute of Technology)
Lily Weng (UCSD)

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