Multi-level Knowledge Distillation via Knowledge Alignment and Correlation

TL;DR

This paper introduces Multi-level Knowledge Distillation (MLKD), a novel approach that combines knowledge alignment and correlation to enhance model compression and transferability across various settings.

Contribution

MLKD effectively integrates both alignment and correlation knowledge transfer, improving upon existing KD methods for diverse models and tasks.

Findings

MLKD outperforms state-of-the-art KD methods in multiple experimental settings.

MLKD improves the reliability and transferability of learned representations.

MLKD is task-agnostic and model-agnostic, compatible with various pretraining strategies.

Abstract

Knowledge distillation (KD) has become an important technique for model compression and knowledge transfer. In this work, we first perform a comprehensive analysis of the knowledge transferred by different KD methods. We demonstrate that traditional KD methods, which minimize the KL divergence of softmax outputs between networks, are related to the knowledge alignment of an individual sample only. Meanwhile, recent contrastive learning-based KD methods mainly transfer relational knowledge between different samples, namely, knowledge correlation. While it is important to transfer the full knowledge from 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 knowledge from supervised or self-supervised pretrained teachers. We show that MLKD can improve the reliability and transferability of learned representations. Experiments demonstrate that MLKD outperforms other state-of-the-art methods on a large number of experimental settings including different (a) pretraining strategies (b) network architectures (c) datasets (d) tasks.