Real-world data often exhibits long-tailed distributions with heavy class
imbalance, posing great challenges for deep recognition models. We identify a
persisting dilemma on the value of labels in the context of imbalanced
learning: on the one hand, supervision from labels typically leads to better
results than its unsupervised counterparts; on the other han