Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer

TL;DR

This paper introduces multiple-exit tuning (MET), a method for vision transformers that improves inference efficiency by allowing easy samples to exit early, reducing computational costs while maintaining high accuracy.

Contribution

The paper proposes a novel multiple-exit tuning approach with shared adapters and graph regularization, enabling efficient inference in vision transformers.

Findings

MET outperforms state-of-the-art methods in accuracy.

MET significantly reduces inference computational cost.

Early exits improve efficiency without sacrificing performance.

Abstract

Parameter-efficient transfer learning (PETL) has shown great potential in adapting a vision transformer (ViT) pre-trained on large-scale datasets to various downstream tasks. Existing studies primarily focus on minimizing the number of learnable parameters. Although these methods are storage-efficient, they allocate excessive computational resources to easy samples, leading to inefficient inference. To address this issue, we introduce an inference-efficient tuning method termed multiple-exit tuning (MET). MET integrates multiple exits into the pre-trained ViT backbone. Since the predictions in ViT are made by a linear classifier, each exit is equipped with a linear prediction head. In inference stage, easy samples will exit at early exits and only hard enough samples will flow to the last exit, thus saving the computational cost for easy samples. MET consists of exit-specific adapters (E-adapters) and graph regularization. E-adapters are designed to extract suitable representations for different exits. To ensure parameter efficiency, all E-adapters share the same down-projection and up-projection matrices. As the performances of linear classifiers are influenced by the relationship among samples, we employ graph regularization to improve the representations fed into the classifiers at early exits. Finally, we conduct extensive experiments to verify the performance of MET. Experimental results show that MET has an obvious advantage over the state-of-the-art methods in terms of both accuracy and inference efficiency.