  Existing knowledge distillation methods typically work by imparting the
knowledge of output logits or intermediate feature maps from the teacher
network to the student network, which is very successful in multi-class
single-label learning. However, these methods can hardly be extended to the
multi-label learning scenario, where each instance is associated with multiple
semantic labels, because the prediction probabilities do not sum to one and
feature maps of the whole example may ignore minor classes in such a scenario.
In this paper, we propose a novel multi-label knowledge distillation method. On
one hand, it exploits the informative semantic knowledge from the logits by
dividing the multi-label learning problem into a set of binary classification
problems; on the other hand, it enhances the distinctiveness of the learned
feature representations by leveraging the structural information of label-wise
embeddings. Experimental results on multiple benchmark datasets validate that
the proposed method can avoid knowledge counteraction among labels, thus
achieving superior performance against diverse comparing methods. Our code is
available at: https://github.com/penghui-yang/L2D
