Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach  

作　　者：ChunhuaWang Wenqian Shang Tong Yi Haibin Zhu 

机构地区：[1]State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing,100024,China [2]School of Computer and Cyber Sciences,Communication University of China,Beijing,100024,China [3]School of Computer Science and Engineering,Guangxi Normal University,Guilin,541004,China [4]Department of Computer Science,Nipissing University,North Bay,ON P1B 8L7,Canada

出　　处：《Computers, Materials & Continua》2024年第5期1939-1956,共18页计算机、材料和连续体（英文）

基　　金：the Communication University of China(CUC230A013);the Fundamental Research Funds for the Central Universities.

摘　　要：The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

关 键 词：Semantic representation sampling attention label-driven co-attention attention mechanisms 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]