Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging

TL;DR

This paper introduces a novel knowledge diversification approach leveraging different learning styles within a combined knowledge distillation and mutual learning framework, improving model performance in biomedical imaging tasks.

Contribution

It proposes a diversified knowledge transfer strategy using predictions and feature maps, enhancing model compression techniques beyond conventional methods.

Findings

Outperforms traditional KD and ML with an average 2% accuracy improvement.

Demonstrates robustness across multiple datasets and network architectures.

Achieves consistent performance gains in classification and segmentation tasks.

Abstract

Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences, like Visual (V), Auditory (A), Read/Write (R), and Kinesthetic (K), for acquiring and effectively processing information. Our work endeavors to leverage this concept of knowledge diversification to improve the performance of model compression techniques like Knowledge Distillation (KD) and Mutual Learning (ML). Consequently, we use a single-teacher and two-student network in a unified framework that not only allows for the transfer of knowledge from teacher to students (KD) but also encourages collaborative learning between students (ML). Unlike the conventional approach, where the teacher shares the same knowledge in the form of predictions or feature representations with the student network, our proposed approach employs a more diversified strategy by training one student with predictions and the other with feature maps from the teacher. We further extend this knowledge diversification by facilitating the exchange of predictions and feature maps between the two student networks, enriching their learning experiences. We have conducted comprehensive experiments with three benchmark datasets for both classification and segmentation tasks using two different network architecture combinations. These experimental results demonstrate that knowledge diversification in a combined KD and ML framework outperforms conventional KD or ML techniques (with similar network configuration) that only use predictions with an average improvement of 2%. Furthermore, consistent improvement in performance across different tasks, with various network architectures, and over state-of-the-art techniques establishes the robustness and generalizability of the proposed model