Reviews: Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

LDAM aims to put regularization on the margins (i.e. the minimum distance of data samples to the decision boundary) of minority classes in order to improve the generalizability of the model towards minority classes during the test time, in which the value of margin is set to be proportional to the number of samples for each class thus the LDAM is label-distribution-aware. DRW runs reweighting and LDAM with smaller learning rate in order to perform fine-tuning on the model after an initial stage of training. Although without any theoretical justification, its efficacy is successfully proven across various experiments. These ideas are novel and shown to provide better superior performance, even avoid overfitting for frequent classes, in comparison to naive re-weighting/re-sampling techniques and other baselines (e.g. It is also required to have an analysis on the sensitivity of performance with respect to C. Additionally, as in both stages of Algorithm.1 LDAM is used, should there be different values of C? - How is the LDAM-HG-DRS in Table.1 implemented?