Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"
Journal:
arXiv
Published Date:
Dec 21, 2024
Abstract
Contrastive learning is a prevalent technique in self-supervised vision
representation learning, typically generating positive pairs by applying two
data augmentations to the same image. Designing effective data augmentation
strategies is crucial for the success of contrastive learning. Inspired by the
story of the blind men and the elephant, we introduce JointCrop and JointBlur.
These methods generate more challenging positive pairs by leveraging the joint
distribution of the two augmentation parameters, thereby enabling contrastive
learning to acquire more effective feature representations. To the best of our
knowledge, this is the first effort to explicitly incorporate the joint
distribution of two data augmentation parameters into contrastive learning. As
a plug-and-play framework without additional computational overhead, JointCrop
and JointBlur enhance the performance of SimCLR, BYOL, MoCo v1, MoCo v2, MoCo
v3, SimSiam, and Dino baselines with notable improvements.
