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The recent studies of knowledge distillation have discovered that ensemblingthe "dark knowledge" from multiple teachers or students contributes to creatingbetter soft targets for training, but at the cost of significantly morecomputations and/or parameters. In this work, we present BAtch KnowledgeEnsembling (BAKE) to produce refined soft targets for anchor images bypropagating and ensembling the knowledge of the other samples in the samemini-batch. Specifically, for each sample of interest, the propagation ofknowledge is weighted in accordance with the inter-sample affinities, which areestimated on-the-fly with the current network. The propagated knowledge canthen be ensembled to form a better soft target for distillation. In this way,our BAKE framework achieves online knowledge ensembling across multiple sampleswith only a single network. It requires minimal computational and memoryoverhead compared to existing knowledge ensembling methods. Extensiveexperiments demonstrate that the lightweight yet effective BAKE consistentlyboosts the classification performance of various architectures on multipledatasets, 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 onlyimprove the vanilla baselines, but also surpasses the single-networkstate-of-the-arts on all the benchmarks.

Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification