Research on transformer and attention in applied algorithms

Abstract

The transformer is an encoder-decoder-based structure and model for deep learning that completely utilizes the self-attention mechanism. It has gained remarkable success in natural language processing and computer vision and is becoming the predominant research direction. This study first analyzes the transformer and attention mechanism, summarizes their advantages, and explores how they help the recommendation algorithm dynamically focus on specific parts of the input that are helpful to perform the current recommendation task. After analyzing the framework of the attention mechanism network and its weight computation for data received. To further enhance the practicality of objects in natural situations and the precision of object recognition, a transformer detection approach based on deformable convolution is presented. And analyzed how the transformer works in the generative pre-trained transformer. These algorithms illustrate the efficacy and robustness of the transformer, indicating that the transformer that incorporates the attention mechanism may satisfy the requirements of the majority of deep learning tasks. However, the unpredictability of demands, the exponential growth of information, and other issues will continue to make it challenging to deal with global interaction mechanisms and a unified framework for multimodal data.