Contrastive self-supervised learning methods learn to map data points such as images into non-parametric representation space without requiring labels. While highly successful, current methods require a large amount of data in the training phase. In situations where the target training set is limited in size, generalization is known to be poor. Pretraining on a large source data set and fine-tuning on the target samples is prone to overfitting in the few-shot regime, where only a small number of target samples are available. Motivated by this, we propose a domain adaption method for self-supervised contrastive learning, termed Few-Max, to address the issue of adaptation to a target distribution under few-shot learning. To quantify the representation quality, we evaluate Few-Max on a range of source and target datasets, including ImageNet, VisDA, and fastMRI, on which Few-Max consistently outperforms other approaches.

本文提出了一种面向Few-Shot自监督对比学习的域自适应方法Few-Max，旨在解决在目标分布下自适应问题。使用各种来源和目标数据集对Few-Max进行了评估，证明其在表示质量方面始终优于其他方法。

Few-Max: 无监督对比表示学习的少样本领域自适应