  Deep learning has achieved remarkable progress for visual recognition on
large-scale balanced datasets but still performs poorly on real-world
long-tailed data. Previous methods often adopt class re-balanced training
strategies to effectively alleviate the imbalance issue, but might be a risk of
over-fitting tail classes. The recent decoupling method overcomes over-fitting
issues by using a multi-stage training scheme, yet, it is still incapable of
capturing tail class information in the feature learning stage. In this paper,
we show that soft label can serve as a powerful solution to incorporate label
correlation into a multi-stage training scheme for long-tailed recognition. The
intrinsic relation between classes embodied by soft labels turns out to be
helpful for long-tailed recognition by transferring knowledge from head to tail
classes.
  Specifically, we propose a conceptually simple yet particularly effective
multi-stage training scheme, termed as Self Supervised to Distillation (SSD).
This scheme is composed of two parts. First, we introduce a self-distillation
framework for long-tailed recognition, which can mine the label relation
automatically. Second, we present a new distillation label generation module
guided by self-supervision. The distilled labels integrate information from
both label and data domains that can model long-tailed distribution
effectively. We conduct extensive experiments and our method achieves the
state-of-the-art results on three long-tailed recognition benchmarks:
ImageNet-LT, CIFAR100-LT and iNaturalist 2018. Our SSD outperforms the strong
LWS baseline by from $2.7\%$ to $4.5\%$ on various datasets. The code is
available at https://github.com/MCG-NJU/SSD-LT.
