Optimization Strategies for Self-Supervised Learning in the Use of Unlabeled Data

Abstract

This study explores optimization strategies for self-supervised learning in the use of unlabeled data. By deeply analyzing existing research, we propose a novel method that significantly enhances the performance of algorithms on unlabeled data, achieving improved accuracy and generalization capabilities. Our method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. We also discuss how to optimize self-supervised learning strategies in the use of unlabeled data. Through improvements and optimizations of self-supervised learning algorithms, we introduce a new method for effectively utilizing unlabeled data for model training. Experimental results show significant performance improvements across various datasets, highlighting the method's robust generalization ability. This research is significant for advancing self-supervised learning technologies, providing valuable insights for related fields.