Augmentation-aware Self-supervised Learning with Conditioned Projector

Understand

Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data.

• By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo can reach quality on par with supervised approaches.
• However, this invariance may be detrimental for solving downstream tasks that depend on traits affected by augmentations used during pretraining, such as color.
• In this paper, we propose to foster sensitivity to such characteristics in the representation space by modifying the projector network, a common component of self-supervised architectures.