  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. We demonstrate how ASL can balance the probabilities of
different samples, and how this balancing is translated to better mAP scores.
With ASL, we reach state-of-the-art results on multiple popular multi-label
datasets: MS-COCO, Pascal-VOC, NUS-WIDE and Open Images. We also demonstrate
ASL applicability for other tasks, such as single-label classification and
object detection. ASL is effective, easy to implement, and does not increase
the training time or complexity.
  Implementation is available at: https://github.com/Alibaba-MIIL/ASL.
