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.

本文研究自我监督对比学习中的两个关键因素：批大小和预设任务，并提出了一种自适应的批融合技术，通过降维和重构批数据，使以前孤立的个体数据能够通过嵌入层在批内进行通信，随着训练进行，自适应地增强自我监督特征编码能力。通过在 ImageNet-1k 上进行线性分类测试，实证结果表明我们的方法在公平比较下实现了最先进的性能。在 ImageNet-100 上，相对于原始性能，top1 最多提高了 1.25％。我们认为该方法可能有助于数据驱动的自我监督学习研究的进展，并为该领域带来新的视角。