SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning

TMLR Paper5253 Authors

Abstract: Contrastive learning has become a dominant approach in self-supervised visual representation learning, but efficiently leveraging hard negatives, which are samples closely resembling the anchor, remains challenging. We introduce SynCo (Synthetic negatives in Contrastive learning), a novel approach that improves model performance by generating synthetic hard negatives on the representation space. Building on the MoCo framework, SynCo introduces six strategies for creating diverse synthetic hard negatives "on-the-fly" with minimal computational overhead. SynCo achieves faster training and strong representation learning, surpassing MoCo-v2 by +0.4% and MoCHI by +1.0% on ImageNet ILSVRC-2012 linear evaluation. It also transfers more effectively to detection tasks achieving strong results on PASCAL VOC detection (57.2% AP) and significantly improving over MoCo-v2 on COCO detection (+1.0% APbb) and instance segmentation (+0.8% APmsk). Our synthetic hard negative generation approach significantly enhances visual representations learned through self-supervised contrastive learning. Code will be made publicly available.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Lei_Wang13

Submission Number: 5253