Contrastive Quant: Quantization Makes Stronger Contrastive Learning
TimeTuesday, July 12th2:37pm - 3pm PDT
Location3000, Level 3
Event Type
Research Manuscript
ML Algorithms and Applications
DescriptionContrastive 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 the 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.