  Contrastive learning has become a key component of self-supervised learning
approaches for computer vision. By learning to embed two augmented versions of
the same image close to each other and to push the embeddings of different
images apart, one can train highly transferable visual representations. As
revealed by recent studies, heavy data augmentation and large sets of negatives
are both crucial in learning such representations. At the same time, data
mixing strategies either at the image or the feature level improve both
supervised and semi-supervised learning by synthesizing novel examples, forcing
networks to learn more robust features. In this paper, we argue that an
important aspect of contrastive learning, i.e., the effect of hard negatives,
has so far been neglected. To get more meaningful negative samples, current top
contrastive self-supervised learning approaches either substantially increase
the batch sizes, or keep very large memory banks; increasing the memory size,
however, leads to diminishing returns in terms of performance. We therefore
start by delving deeper into a top-performing framework and show evidence that
harder negatives are needed to facilitate better and faster learning. Based on
these observations, and motivated by the success of data mixing, we propose
hard negative mixing strategies at the feature level, that can be computed
on-the-fly with a minimal computational overhead. We exhaustively ablate our
approach on linear classification, object detection and instance segmentation
and show that employing our hard negative mixing procedure improves the quality
of visual representations learned by a state-of-the-art self-supervised
learning method.
