  The recent studies of knowledge distillation have discovered that ensembling
the "dark knowledge" from multiple teachers or students contributes to creating
better soft targets for training, but at the cost of significantly more
computations and/or parameters. In this work, we present BAtch Knowledge
Ensembling (BAKE) to produce refined soft targets for anchor images by
propagating and ensembling the knowledge of the other samples in the same
mini-batch. Specifically, for each sample of interest, the propagation of
knowledge is weighted in accordance with the inter-sample affinities, which are
estimated on-the-fly with the current network. The propagated knowledge can
then be ensembled to form a better soft target for distillation. In this way,
our BAKE framework achieves online knowledge ensembling across multiple samples
with only a single network. It requires minimal computational and memory
overhead compared to existing knowledge ensembling methods. Extensive
experiments demonstrate that the lightweight yet effective BAKE consistently
boosts the classification performance of various architectures on multiple
datasets, e.g., a significant +0.7% gain of Swin-T on ImageNet with only +1.5%
computational overhead and zero additional parameters. BAKE does not only
improve the vanilla baselines, but also surpasses the single-network
state-of-the-arts on all the benchmarks.
