Aligning in a Compact Space: Contrastive Knowledge Distillation between Heterogeneous Architectures

TL;DR

This paper introduces LFCC, a novel contrastive knowledge distillation method that aligns low-frequency features across heterogeneous neural network architectures, significantly improving performance on image classification benchmarks.

Contribution

The paper proposes a low-frequency component-based contrastive distillation framework that effectively bridges architectural differences in neural networks, enhancing feature alignment and model performance.

Findings

LFCC outperforms existing methods on ImageNet-1K and CIFAR-100.

It effectively aligns features across CNNs, Transformers, and MLPs.

The approach improves student model accuracy with heterogeneous teachers.

Abstract

Knowledge distillation is commonly employed to compress neural networks, reducing the inference costs and memory footprint. In the scenario of homogenous architecture, feature-based methods have been widely validated for their effectiveness. However, in scenarios where the teacher and student models are of heterogeneous architectures, the inherent differences in feature representation significantly degrade the performance of these methods. Recent studies have highlighted that low-frequency components constitute the majority of image features. Motivated by this, we propose a Low-Frequency Components-based Contrastive Knowledge Distillation (LFCC) framework that significantly enhances the performance of feature-based distillation between heterogeneous architectures. Specifically, we designe a set of multi-scale low-pass filters to extract the low-frequency components of intermediate features from both the teacher and student models, aligning them in a compact space to overcome architectural disparities. Moreover, leveraging the intrinsic pairing characteristic of the teacher-student framework, we design an innovative sample-level contrastive learning framework that adeptly restructures the constraints of within-sample feature similarity and between-sample feature divergence into a contrastive learning task. This strategy enables the student model to capitalize on intra-sample feature congruence while simultaneously enhancing the discrimination of features among disparate samples. Consequently, our LFCC framework accurately captures the commonalities in feature representation across heterogeneous architectures. Extensive evaluations and empirical analyses across three architectures (CNNs, Transformers, and MLPs) demonstrate that LFCC achieves superior performance on the challenging benchmarks of ImageNet-1K and CIFAR-100. All codes will be publicly available.