  The networks trained on the long-tailed dataset vary remarkably, despite the
same training settings, which shows the great uncertainty in long-tailed
learning. To alleviate the uncertainty, we propose a Nested Collaborative
Learning (NCL), which tackles the problem by collaboratively learning multiple
experts together. NCL consists of two core components, namely Nested Individual
Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on
the individual supervised learning for each single expert and the knowledge
transferring among multiple experts, respectively. To learn representations
more thoroughly, both NIL and NBOD are formulated in a nested way, in which the
learning is conducted on not just all categories from a full perspective but
some hard categories from a partial perspective. Regarding the learning in the
partial perspective, we specifically select the negative categories with high
predicted scores as the hard categories by using a proposed Hard Category
Mining (HCM). In the NCL, the learning from two perspectives is nested, highly
related and complementary, and helps the network to capture not only global and
robust features but also meticulous distinguishing ability. Moreover,
self-supervision is further utilized for feature enhancement. Extensive
experiments manifest the superiority of our method with outperforming the
state-of-the-art whether by using a single model or an ensemble.
