  Contrastive learning has emerged as an essential approach for self-supervised
learning in visual representation learning. The central objective of
contrastive learning is to maximize the similarities between two augmented
versions of an image (positive pairs), while minimizing the similarities
between different images (negative pairs). Recent studies have demonstrated
that harder negative samples, i.e., those that are more difficult to
differentiate from the anchor sample, perform a more crucial function in
contrastive learning. This paper proposes a novel feature-level method, namely
sampling synthetic hard negative samples for contrastive learning (SSCL), to
exploit harder negative samples more effectively. Specifically, 1) we generate
more and harder negative samples by mixing negative samples, and then sample
them by controlling the contrast of anchor sample with the other negative
samples; 2) considering the possibility of false negative samples, we further
debias the negative samples. Our proposed method improves the classification
performance on different image datasets and can be readily integrated into
existing methods.
