  Self-supervised learning (SSL) has recently shown tremendous potential to
learn generic visual representations useful for many image analysis tasks.
Despite their notable success, the existing SSL methods fail to generalize to
downstream tasks when the number of labeled training instances is small or if
the domain shift between the transfer domains is significant. In this paper, we
attempt to improve self-supervised pretrained representations through the lens
of curriculum learning by proposing a hardness-aware dynamic curriculum
learning (HaDCL) approach. To improve the robustness and generalizability of
SSL, we dynamically leverage progressive harder examples via easy-to-hard and
hard-to-very-hard samples during mini-batch downstream fine-tuning. We discover
that by progressive stage-wise curriculum learning, the pretrained
representations are significantly enhanced and adaptable to both in-domain and
out-of-domain distribution data.
  We performed extensive validation on three histology benchmark datasets on
both patch-wise and slide-level classification problems. Our curriculum based
fine-tuning yields a significant improvement over standard fine-tuning, with a
minimum improvement in area-under-the-curve (AUC) score of 1.7% and 2.2% on
in-domain and out-of-domain distribution data, respectively. Further, we
empirically show that our approach is more generic and adaptable to any SSL
methods and does not impose any additional overhead complexity. Besides, we
also outline the role of patch-based versus slide-based curriculum learning in
histopathology to provide practical insights into the success of curriculum
based fine-tuning of SSL methods. Code is released at
https://github.com/srinidhiPY/ICCV-CDPATH2021-ID-8
