Adaptive Teaching with Shared Classifier for Knowledge Distillation

TL;DR

This paper introduces ATSC, an adaptive knowledge distillation method that dynamically aligns teacher networks with student learning needs using shared classifiers, achieving state-of-the-art results on CIFAR-100 and ImageNet.

Contribution

The paper proposes a novel adaptive teaching approach with shared classifiers that self-adjusts the teacher network for improved student learning, extending to multi-teacher environments.

Findings

Achieves state-of-the-art results on CIFAR-100 and ImageNet datasets.

Effective in both single-teacher and multi-teacher scenarios.

Requires only a modest increase in model parameters.

Abstract

Knowledge distillation (KD) is a technique used to transfer knowledge from an overparameterized teacher network to a less-parameterized student network, thereby minimizing the incurred performance loss. KD methods can be categorized into offline and online approaches. Offline KD leverages a powerful pretrained teacher network, while online KD allows the teacher network to be adjusted dynamically to enhance the learning effectiveness of the student network. Recently, it has been discovered that sharing the classifier of the teacher network can significantly boost the performance of the student network with only a minimal increase in the number of network parameters. Building on these insights, we propose adaptive teaching with a shared classifier (ATSC). In ATSC, the pretrained teacher network self-adjusts to better align with the learning needs of the student network based on its capabilities, and the student network benefits from the shared classifier, enhancing its performance. Additionally, we extend ATSC to environments with multiple teachers. We conduct extensive experiments, demonstrating the effectiveness of the proposed KD method. Our approach achieves state-of-the-art results on the CIFAR-100 and ImageNet datasets in both single-teacher and multiteacher scenarios, with only a modest increase in the number of required model parameters. The source code is publicly available at https://github.com/random2314235/ATSC.