Asymmetric Loss For Multi-Label Classification

Understand

In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones.

• This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradients from positive labels during training, resulting in poor accuracy.
• In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
• The loss enables to dynamically down-weights and hard-thresholds easy negative samples, while also discarding possibly mislabeled samples.