Improvement of Attentional Mechanisms for Unsupervised Learning Models

Image recognition based on unsupervised learning is a worthy direction of research. Inspired by ImageNet and other open source image sets, more and more unsupervised learning methods have been applied to image recognition and achieved good results. Based on the unsupervised learning model based on momentum contrast, the attention mechanism module is introduced, and a new parameter adjustment strategy for learning rate is proposed for further optimization of the algorithm. In the module of attentional mechanism, self-SE attentional mechanism model is proposed by integrating self-attention and SENet models. Batch-size Warmup parameter adjustment strategy is proposed in this paper to further improve the performance of the model. In this paper, the proposed improved module and adjustment strategy are verified by several groups of experiments with CIFAR-100 as data set. Compared with the original method, the accuracy of the unsupervised learning image recognition method with improved modules and adjusted strategies has been improved by about 1.2%.