Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition. (arXiv:2207.10299v2 [cs.CV] UPDATED)

Noisy label Facial Expression Recognition (FER) is more challenging than
traditional noisy label classification tasks due to the inter-class similarity
and the annotation ambiguity. Recent works mainly tackle this problem by
filtering out large-loss samples. In this paper, we explore dealing with noisy
labels from a new feature-learning perspective. We find that FER models
remember noisy samples by focusing on a part of the features that can be
considered related to the noisy labels instead of learning from the whole …

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