Prototype-Guided Dynamic Margin Learning for Imbalanced Time-Series Classification
Time-series classifiers trained with cross-entropy often inherit the class-frequency bias of the training set, especially when rare classes are also poorly separated in the latent feature space. This paper presents ProtoMargin, a prototype-guided dynamic margin loss for imbalanced time-series classification. The loss m...