Loss re-weighting Apart from the aforementioned databased re-balance strategies, another line of studies propose to mitigate the negative effects of **data** imbalance by modifying the loss functions. Loss re-weighting is one of the simple but effective ways to tailor the loss function for imbalanced classification, where the basic idea is to upweight the tailed samples and downweight the head samples in the loss function [17]. The existing solutions differ mainly in how to define the weights for different classes. In classsensitive cross-entropy loss [14], the weight assigned to each class is inversely proportional to the number of samples. In class-balanced loss [7], the authors decide the re-weighting coefficients based on the real volumes of different classes, named effective numbers. In the work [30], the weights to the training examples are optimized to minimize the loss of a held-out evaluation set.