Image Classification Method Base on Contrastive Learning

This paper presents an innovative method for self-supervised learning in image classification, leveraging the contrastive learning paradigm. The proposed framework incorporates the SEResNet50 backbone and employs the contrastive loss function to facilitate the learning of discriminative features. Additionally, a local self-attention mechanism is introduced in the classification head following transfer learning. This work contributes in two main aspects: firstly, the proposed framework demonstrates an approximate 3% improvement in classification accuracy compared to the baseline approach; secondly, it significantly reduces training time and enhances convergence rate. Experimental evaluations on the STL-10 dataset validate the superior performance of the proposed framework over the baseline approach. Moreover, the local self-attention mechanism proves to be effective in enhancing the discriminative power of the learned features. In conclusion, this paper introduces a novel framework for self-supervised learning, which combines the SEResNet50 backbone, contrastive loss function, and local self-attention mechanism. The proposed approach achieves exceptional performance and reduces training time, thus exhibiting great promise for image classification tasks.