Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation

TL;DR

This paper introduces a tensor decomposition method to over-parameterize student models in knowledge distillation, significantly improving their performance without increasing inference latency across vision and NLP tasks.

Contribution

It proposes a novel tensor decomposition strategy for over-parameterizing student models in knowledge distillation, maintaining efficiency and enhancing performance.

Findings

Enhanced student model accuracy in vision tasks

Improved NLP task performance

Effective tensor constraint loss implementation

Abstract

Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model, facilitating the transfer of knowledge of large models. In contrast to much of the previous work, we scale up the parameters of the student model during training, to benefit from overparameterization without increasing the inference latency. In particular, we propose a tensor decomposition strategy that effectively over-parameterizes the relatively small student model through an efficient and nearly lossless decomposition of its parameter matrices into higher-dimensional tensors. To ensure efficiency, we further introduce a tensor constraint loss to align the high-dimensional tensors between the student and teacher models. Comprehensive experiments validate the significant performance enhancement by our approach in various KD tasks, covering computer vision and natural language processing areas. Our code is available at https://github.com/intell-sci-comput/OPDF.