  Knowledge Distillation (KD) has been one of the most popu-lar methods to
learn a compact model. However, it still suffers from highdemand in time and
computational resources caused by sequential train-ing pipeline. Furthermore,
the soft targets from deeper models do notoften serve as good cues for the
shallower models due to the gap of com-patibility. In this work, we consider
these two problems at the same time.Specifically, we propose that better soft
targets with higher compatibil-ity can be generated by using a label generator
to fuse the feature mapsfrom deeper stages in a top-down manner, and we can
employ the meta-learning technique to optimize this label generator. Utilizing
the softtargets learned from the intermediate feature maps of the model, we
canachieve better self-boosting of the network in comparison with the
state-of-the-art. The experiments are conducted on two standard
classificationbenchmarks, namely CIFAR-100 and ILSVRC2012. We test various
net-work architectures to show the generalizability of our MetaDistiller.
Theexperiments results on two datasets strongly demonstrate the effective-ness
of our method.
