  Neural networks trained on real-world datasets with long-tailed label
distributions are biased towards frequent classes and perform poorly on
infrequent classes. The imbalance in the ratio of positive and negative samples
for each class skews network output probabilities further from ground-truth
distributions. We propose a method, Partial Label Masking (PLM), which utilizes
this ratio during training. By stochastically masking labels during loss
computation, the method balances this ratio for each class, leading to improved
recall on minority classes and improved precision on frequent classes. The
ratio is estimated adaptively based on the network's performance by minimizing
the KL divergence between predicted and ground-truth distributions. Whereas
most existing approaches addressing data imbalance are mainly focused on
single-label classification and do not generalize well to the multi-label case,
this work proposes a general approach to solve the long-tail data imbalance
issue for multi-label classification. PLM is versatile: it can be applied to
most objective functions and it can be used alongside other strategies for
class imbalance. Our method achieves strong performance when compared to
existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label
(imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
