An Efficient Attention Mechanism Model

This article presents an innovative approach to enhance the efficiency of attention mechanism-based models. Our primary goal is to reduce computational complexity and running time while maintaining the performance integrity. We achieve this by streamlining models through the elimination of redundant attention heads, making them more resource-efficient across various tasks. In this paper, we focus on optimizing Bottleneck Transformers (BoTNet) and Attention Augmented Convolutional Networks (AACN), both widely exploited in the computer vision applications and based on Multi-Head Self Attention (MHSA). We introduce a novel head pruning technique. Instead of using a fixed number of attention heads, our method adaptively selects the most valuable heads during training. This dynamic approach significantly reduces computational overhead, expediting model convergence and shortening training time. Extensive experiments on benchmarks like ImageNet, Pascal VOC, CIFAR-10 and Common Objects in Context (COCO) datasets prove the effectiveness of our approach. These refined models not only match the performance of their more complex counterparts but also exhibit faster convergence during training. This expedites model transfer and deployment, making our approach practical across a range of applications.