Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

NeurIPS 2019 Kaidi CaoColin WeiAdrien GaidonNikos ArechigaTengyu Ma

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios... (read more)

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