基于Swin Transformer的自监督学习

Self-supervised learning is an emerging machine learning approach that leverages unlabeled data for model training. The Swin Transformer, a visual transformer architecture, possesses hierarchical feature representation capabilities suitable for image data. This research proposes a self-supervised learning framework based on the Swin Transformer, aimed at enhancing performance in image understanding tasks. By designing effective pre-training tasks, the framework can be trained on large-scale unlabeled datasets, thereby improving the model's generalization ability and accuracy. Experimental results demonstrate that this method performs exceptionally well across multiple benchmark datasets, showcasing the potential of self-supervised learning in visual tasks.