Improved Knowledge Distillation via Full Kernel Matrix Transfer

TL;DR

This paper introduces an efficient method for knowledge distillation that transfers the full similarity matrix between teacher and student models using Nyström decomposition, improving performance and efficiency.

Contribution

It proposes a novel approach to transfer the full kernel similarity matrix via Nyström approximation, enhancing distillation effectiveness and computational efficiency.

Findings

Effective transfer of full similarity matrix improves student model performance.

Nyström method reduces computational complexity from quadratic to linear.

Empirical results demonstrate superior performance on benchmark datasets.

Abstract

Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model (i.e., student model) by transferring the information from the teacher. Various information for distillation has been studied. Recently, a number of works propose to transfer the pairwise similarity between examples to distill relative information. However, most of efforts are devoted to developing different similarity measurements, while only a small matrix consisting of examples within a mini-batch is transferred at each iteration that can be inefficient for optimizing the pairwise similarity over the whole data set. In this work, we aim to transfer the full similarity matrix effectively. The main challenge is from the size of the full matrix that is quadratic to the number of examples. To address the challenge, we decompose the original full matrix with Nystr{\"{o}}m method. By selecting appropriate landmark points, our theoretical analysis indicates that the loss for transfer can be further simplified. Concretely, we find that the difference between the original full kernel matrices between teacher and student can be well bounded by that of the corresponding partial matrices, which only consists of similarities between original examples and landmark points. Compared with the full matrix, the size of the partial matrix is linear in the number of examples, which improves the efficiency of optimization significantly. The empirical study on benchmark data sets demonstrates the effectiveness of the proposed algorithm. Code is available at \url{https://github.com/idstcv/KDA}.