  In recent years, self-supervised contrastive learning has emerged as a
distinguished paradigm in the artificial intelligence landscape. It facilitates
unsupervised feature learning through contrastive delineations at the instance
level. However, crafting an effective self-supervised paradigm remains a
pivotal challenge within this field. This paper delves into two crucial factors
impacting self-supervised contrastive learning-bach size and pretext tasks, and
from a data processing standpoint, proposes an adaptive technique of batch
fusion. The proposed method, via dimensionality reduction and reconstruction of
batch data, enables formerly isolated individual data to partake in intra-batch
communication through the Embedding Layer. Moreover, it adaptively amplifies
the self-supervised feature encoding capability as the training progresses. We
conducted a linear classification test of this method based on the classic
contrastive learning framework on ImageNet-1k. The empirical findings
illustrate that our approach achieves state-of-the-art performance under
equitable comparisons. Benefiting from its "plug-and-play" characteristics, we
further explored other contrastive learning methods. On the ImageNet-100,
compared to the original performance, the top1 has seen a maximum increase of
1.25%. We suggest that the proposed method may contribute to the advancement of
data-driven self-supervised learning research, bringing a fresh perspective to
this community.
