Knowledge Distillation Layer that Lets the Student Decide

TL;DR

This paper introduces a learnable knowledge distillation layer that allows the student model to explicitly decide how to leverage teacher knowledge, improving feature transfer and performance during inference.

Contribution

It proposes a novel KD layer that enables the student to learn how to utilize teacher knowledge explicitly, enhancing intermediate feature transfer and inference capabilities.

Findings

Improved accuracy on 3 classification benchmarks

Effective feature transfer in intermediate layers

Learned templates improve knowledge utilization

Abstract

Typical technique in knowledge distillation (KD) is regularizing the learning of a limited capacity model (student) by pushing its responses to match a powerful model's (teacher). Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate layers. To explicitly embed the teacher's knowledge in feature transform, we propose a learnable KD layer for the student which improves KD with two distinct abilities: i) learning how to leverage the teacher's knowledge, enabling to discard nuisance information, and ii) feeding forward the transferred knowledge deeper. Thus, the student enjoys the teacher's knowledge during the inference besides training. Formally, we repurpose 1x1-BN-ReLU-1x1 convolution block to assign a semantic vector to each local region according to the template (supervised by the teacher) that the corresponding region of the student matches. To facilitate template learning in the intermediate layers, we propose a novel form of supervision based on the teacher's decisions. Through rigorous experimentation, we demonstrate the effectiveness of our approach on 3 popular classification benchmarks. Code is available at: https://github.com/adagorgun/letKD-framework