Adaptive Attention Link-based Regularization for Vision Transformers

TL;DR

This paper introduces an adaptive regularization method using trainable links between CNN and ViT attention mechanisms, enhancing training efficiency and performance of Vision Transformers with limited data.

Contribution

It proposes a novel attention augmentation module that improves ViT training efficiency and performance, especially with small datasets, by leveraging CNN attention relationships.

Findings

Improves ViT performance with limited data

Accelerates convergence during training

Reduces overfitting in Vision Transformers

Abstract

Although transformer networks are recently employed in various vision tasks with outperforming performance, extensive training data and a lengthy training time are required to train a model to disregard an inductive bias. Using trainable links between the channel-wise spatial attention of a pre-trained Convolutional Neural Network (CNN) and the attention head of Vision Transformers (ViT), we present a regularization technique to improve the training efficiency of ViT. The trainable links are referred to as the attention augmentation module, which is trained simultaneously with ViT, boosting the training of ViT and allowing it to avoid the overfitting issue caused by a lack of data. From the trained attention augmentation module, we can extract the relevant relationship between each CNN activation map and each ViT attention head, and based on this, we also propose an advanced attention augmentation module. Consequently, even with a small amount of data, the suggested method considerably improves the performance of ViT while achieving faster convergence during training.