This article introduces a novel approach to self-supervised learning by focusing on batch fusion and reconstruction. It addresses the challenges of pretext task design and batch size in self-supervised learning. The proposed method, Batch-Adaptive Self-Supervised Learning (BA-SSL), integrates batch information effectively to enhance feature representation capabilities. The article outlines the method's architecture, including Patch Partition, Conv Embedding, and Patch Restore. Empirical findings demonstrate the state-of-the-art performance of BA-SSL on ImageNet-1k and ImageNet-100 datasets. The method is also shown to be a plug-and-play solution for enhancing existing self-supervised learning models. Furthermore, the impact of the number of Embedding Layers on model performance is explored. The article concludes by discussing the challenges and future directions in self-supervised learning research.
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