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.

翻译：自监督对比学习高度依赖于数据增强带来的视角差异，从而能够学习到视角不变的预训练表示。除了增加对比的视角差异外，本研究重点关注提升训练数据的多样性，以改善预训练模型的泛化能力和鲁棒性。为此，我们提出了一个在特征空间中进行数据增强的统一框架，即特征增强。该策略是领域无关的，它生成与原始特征相似的特征，从而提高了数据多样性。我们对多种特征增强架构、梯度流技巧以及特征增强与传统数据增强之间的关系进行了系统性研究。我们的研究揭示了自对比学习中特征增强的一些实用原则。通过在实例判别或实例相似性范式中集成特征增强，我们持续提升了预训练特征学习的性能，并在下游图像分类和物体检测任务上获得了更好的泛化能力。