  Real-world data usually present long-tailed distributions. Training on
imbalanced data tends to render neural networks perform well on head classes
while much worse on tail classes. The severe sparseness of training instances
for the tail classes is the main challenge, which results in biased
distribution estimation during training. Plenty of efforts have been devoted to
ameliorating the challenge, including data re-sampling and synthesizing new
training instances for tail classes. However, no prior research has exploited
the transferable knowledge from head classes to tail classes for calibrating
the distribution of tail classes. In this paper, we suppose that tail classes
can be enriched by similar head classes and propose a novel distribution
calibration approach named as label-Aware Distribution Calibration LADC. LADC
transfers the statistics from relevant head classes to infer the distribution
of tail classes. Sampling from calibrated distribution further facilitates
re-balancing the classifier. Experiments on both image and text long-tailed
datasets demonstrate that LADC significantly outperforms existing methods.The
visualization also shows that LADC provides a more accurate distribution
estimation.
