  Self-supervised learning (SSL) is a powerful technique for learning robust
representations from unlabeled data. By learning to remain invariant to applied
data augmentations, methods such as SimCLR and MoCo are able to reach quality
on par with supervised approaches. However, this invariance may be harmful to
solving some downstream tasks which 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.
Specifically, we supplement the projector with information about augmentations
applied to images. In order for the projector to take advantage of this
auxiliary conditioning when solving the SSL task, the feature extractor learns
to preserve the augmentation information in its representations. Our approach,
coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is
directly applicable to typical joint-embedding SSL methods regardless of their
objective functions. Moreover, it does not require major changes in the network
architecture or prior knowledge of downstream tasks. In addition to an analysis
of sensitivity towards different data augmentations, we conduct a series of
experiments, which show that CASSLE improves over various SSL methods, reaching
state-of-the-art performance in multiple downstream tasks.
