Adaptive Attention Link-based Regularization for Vision Transformers
This work addresses the training inefficiency and overfitting issues in Vision Transformers for computer vision tasks, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of Vision Transformers requiring extensive training data and time by introducing an adaptive attention link-based regularization technique that uses trainable links between a pre-trained CNN and ViT attention heads. The method significantly improves ViT performance and achieves faster convergence even with small datasets.
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.