Self-supervised learning aims to learn a embedding space where semantically similar samples are close. Contrastive learning methods pull views of samples together and push different samples away, which utilizes semantic invariance of augmentation but ignores the relationship between samples. To better exploit the power of augmentation, we observe that semantically similar samples are more likely to have similar augmented views. Therefore, we can take the augmented views as a special description of a sample. In this paper, we model such a description as the augmentation distribution and we call it augmentation feature. The similarity in augmentation feature reflects how much the views of two samples overlap and is related to their semantical similarity. Without computational burdens to explicitly estimate values of the augmentation feature, we propose Augmentation Component Analysis (ACA) with a contrastive-like loss to learn principal components and an on-the-fly projection loss to embed data. ACA equals an efficient dimension reduction by PCA and extracts low-dimensional embeddings, theoretically preserving the similarity of augmentation distribution between samples. Empirical results show our method can achieve competitive results against various traditional contrastive learning methods on different benchmarks.

翻译：自监督学习旨在学习一个嵌入空间，使得语义相似的样本在该空间中彼此接近。对比学习方法将样本的增强视图相互拉近，同时将不同样本推开，这利用了增强的语义不变性，但忽略了样本之间的关系。为了更好地发挥增强的作用，我们观察到语义相似的样本更有可能具有相似的增强视图。因此，我们可以将增强视图视为样本的一种特殊描述。在本文中，我们将这种描述建模为增强分布，并称之为增强特征。增强特征中的相似性反映了两个样本视图的重叠程度，并与其语义相似性相关。为了在无需显式计算增强特征值的计算负担下，我们提出了增强成分分析（ACA），该方法采用类似对比学习的损失来学习主成分，并使用即时投影损失来嵌入数据。ACA等同于通过PCA进行高效的降维，并提取低维嵌入，理论上保留了样本间增强分布的相似性。实验结果表明，我们的方法在不同基准测试中能够与多种传统对比学习方法取得有竞争力的结果。