  In real-world scenarios, data tends to exhibit a long-tailed distribution,
which increases the difficulty of training deep networks. In this paper, we
propose a novel self-paced knowledge distillation framework, termed Learning
From Multiple Experts (LFME). Our method is inspired by the observation that
networks trained on less imbalanced subsets of the distribution often yield
better performances than their jointly-trained counterparts. We refer to these
models as 'Experts', and the proposed LFME framework aggregates the knowledge
from multiple 'Experts' to learn a unified student model. Specifically, the
proposed framework involves two levels of adaptive learning schedules:
Self-paced Expert Selection and Curriculum Instance Selection, so that the
knowledge is adaptively transferred to the 'Student'. We conduct extensive
experiments and demonstrate that our method is able to achieve superior
performances compared to state-of-the-art methods. We also show that our method
can be easily plugged into state-of-the-art long-tailed classification
algorithms for further improvements.
