  Self-supervised learning has recently achieved great success in
representation learning without human annotations. The dominant method -- that
is contrastive learning, is generally based on instance discrimination tasks,
i.e., individual samples are treated as independent categories. However,
presuming all the samples are different contradicts the natural grouping of
similar samples in common visual datasets, e.g., multiple views of the same
dog. To bridge the gap, this paper proposes an adaptive method that introduces
soft inter-sample relations, namely Adaptive Soft Contrastive Learning (ASCL).
More specifically, ASCL transforms the original instance discrimination task
into a multi-instance soft discrimination task, and adaptively introduces
inter-sample relations. As an effective and concise plug-in module for existing
self-supervised learning frameworks, ASCL achieves the best performance on
several benchmarks in terms of both performance and efficiency. Code is
available at https://github.com/MrChenFeng/ASCL_ICPR2022.
