Multi-view Teacher-Student Network

Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for performance improvement. Knowledge Distillation (KD), characterized by the so-called Teacher-Student (T-S) learning framework, can transfer information learned from one model to another. Inspired by knowledge distillation, we propose a Multi-view Teacher-Student Network (MTS-Net), which combines knowledge distillation and multi-view learning into a unified framework. We first redefine the teacher and student for the multi-view case. Then the MTS-Net is built by optimizing both the view classification loss and the knowledge distillation loss in an end-to-end training manner. We further extend MTS-Net to image recognition tasks and present a multi-view Teacher-Student framework with convolutional neural networks called MTSCNN. To the best of our knowledge, MTS-Net and MTSCNN bring a new insight to extend the Teacher-Student framework to tackle the multi-view learning problem. We theoretically verify the mechanism of MTS-Net and MTSCNN and comprehensive experiments demonstrate the effectiveness of the proposed methods. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a multi-view Teacher-Student network framework is proposed to combine knowledge distillation and multi-view learning, effectively addressing the issue of multi-view learning. By redefining teachers and students and training in an end-to-end manner, MTS-Net was established.