Self-supervised contrastive learning heavily relies on the view variance brought by data augmentation, so that it can learn a view-invariant pre-trained representation. Beyond increasing the view variance for contrast, this work focuses on improving the diversity of training data, to improve the generalization and robustness of the pre-trained models. To this end, we propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity. We perform a systematic investigation of various feature augmentation architectures, the gradient-flow skill, and the relationship between feature augmentation and traditional data augmentation. Our study reveals some practical principles for feature augmentation in self-contrastive learning. By integrating feature augmentation on the instance discrimination or the instance similarity paradigm, we consistently improve the performance of pre-trained feature learning and gain better generalization over the downstream image classification and object detection task.

本研究针对自监督对比学习中数据增强引入的视图变异性问题，提出特征增强方法，以提升训练数据的多样性，进而增强预训练模型的泛化能力和鲁棒性。研究显示，将特征增强集成到实例区分或实例相似性范式中，可以显著提升预训练特征学习的表现，并在下游图像分类和目标检测任务中实现更好的泛化效果。

自监督对比学习的特征增强：更深入的探讨