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Title: Contrastive quant: quantization makes stronger contrastive learning
Contrastive learning learns visual representations by enforcing feature consistency under different augmented views. In this work, we explore contrastive learning from a new perspective. Interestingly, we find that quantization, when properly engineered, can enhance the effectiveness of contrastive learning. To this end, we propose a novel contrastive learning framework, dubbed Contrastive Quant, to encourage feature consistency under both differently augmented inputs via various data transformations and differently augmented weights/activations via various quantization levels. Extensive experiments, built on top of two state-of-the-art contrastive learning methods SimCLR and BYOL, show that Contrastive Quant consistently improves the learned visual representation.  more » « less
Award ID(s):
1934767 2048183
NSF-PAR ID:
10357327
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
Page Range / eLocation ID:
205 to 210
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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