SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image ReconstructionDownload PDF

Published: 04 Apr 2023, Last Modified: 17 Sept 2023MIDL 2023 PosterReaders: Everyone
Keywords: Knowledge Distillation (KD), Student-Friendly Teacher KD framework (SFT- KD Recon), MRI, deep cascaded convolutional neural networks (DC-CNN)
TL;DR: A student-friendly teacher training approach along with the student as a prior step to Knowledge distillation to enable aligning the teacher’s representations with the student for effective distillation.
Abstract: Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillation (KD) is an emerging technique to compress these models, in which a trained deep `teacher' network is used to distill knowledge to a smaller `student' network, such that the student learns to mimic the behavior of the teacher. Most KD methods focus on effectively training the student with a pre-trained teacher that is unaware of the student model. We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the student's structure and capacity and enable aligning the teacher's representations with the student. In SFT, the teacher is jointly trained with the unfolded branch configurations of the student blocks using three loss terms - teacher-reconstruction loss, student-reconstruction loss, and teacher-student imitation loss, followed by KD of the student. We perform extensive experiments for MRI acceleration in 4x and 5x under-sampling, on the brain and cardiac datasets on five KD methods using the proposed approach as a prior step. We consider the DC-CNN architecture and setup teacher as D5C5 (141765 parameters), and student as D3C5 (49285 parameters) denoting 2.87:1 compression. Results show that (i) our approach consistently improves the KD methods with improved reconstruction performance and image quality, and (ii) the student distilled using our approach is competitive with the teacher, with the performance gap reduced from 0.53 dB to 0.03 dB.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2304.05057/code)
4 Replies