  Data collected from the real world typically exhibit long-tailed
distributions, where frequent classes contain abundant data while rare ones
have only a limited number of samples. While existing supervised learning
approaches have been proposed to tackle such data imbalance, the requirement of
label supervision would limit their applicability to real-world scenarios in
which label annotation might not be available. Without the access to class
labels nor the associated class frequencies, we propose Frequency-Aware
Self-Supervised Learning (FASSL) in this paper. Targeting at learning from
unlabeled data with inherent long-tailed distributions, the goal of FASSL is to
produce discriminative feature representations for downstream classification
tasks. In FASSL, we first learn frequency-aware prototypes, reflecting the
associated long-tailed distribution. Particularly focusing on rare-class
samples, the relationships between image data and the derived prototypes are
further exploited with the introduced self-supervised learning scheme.
Experiments on long-tailed image datasets quantitatively and qualitatively
verify the effectiveness of our learning scheme.
