  Deep neural networks have made huge progress in the last few decades.
However, as the real-world data often exhibits a long-tailed distribution,
vanilla deep models tend to be heavily biased toward the majority classes. To
address this problem, state-of-the-art methods usually adopt a mixture of
experts (MoE) to focus on different parts of the long-tailed distribution.
Experts in these methods are with the same model depth, which neglects the fact
that different classes may have different preferences to be fit by models with
different depths. To this end, we propose a novel MoE-based method called
Self-Heterogeneous Integration with Knowledge Excavation (SHIKE). We first
propose Depth-wise Knowledge Fusion (DKF) to fuse features between different
shallow parts and the deep part in one network for each expert, which makes
experts more diverse in terms of representation. Based on DKF, we further
propose Dynamic Knowledge Transfer (DKT) to reduce the influence of the hardest
negative class that has a non-negligible impact on the tail classes in our MoE
framework. As a result, the classification accuracy of long-tailed data can be
significantly improved, especially for the tail classes. SHIKE achieves the
state-of-the-art performance of 56.3%, 60.3%, 75.4%, and 41.9% on CIFAR100-LT
(IF100), ImageNet-LT, iNaturalist 2018, and Places-LT, respectively.
