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Knowledge distillation (KD) has become an important technique for modelcompression and knowledge transfer. In this work, we first perform acomprehensive analysis of the knowledge transferred by different KD methods. Wedemonstrate that traditional KD methods, which minimize the KL divergence ofsoftmax outputs between networks, are related to the knowledge alignment of anindividual sample only. Meanwhile, recent contrastive learning-based KD methodsmainly transfer relational knowledge between different samples, namely,knowledge correlation. While it is important to transfer the full knowledgefrom teacher to student, we introduce the Multi-level Knowledge Distillation(MLKD) by effectively considering both knowledge alignment and correlation.MLKD is task-agnostic and model-agnostic, and can easily transfer knowledgefrom supervised or self-supervised pretrained teachers. We show that MLKD canimprove the reliability and transferability of learned representations.Experiments demonstrate that MLKD outperforms other state-of-the-art methods ona large number of experimental settings including different (a) pretrainingstrategies (b) network architectures (c) datasets (d) tasks.

Multi-level Knowledge Distillation via Knowledge Alignment and Correlation