Learning a data representation for downstream supervised learning tasks under unlabeled scenario is both critical and challenging. In this paper, we propose a novel unsupervised transfer learning approach using adversarial contrastive training (ACT). Our experimental results demonstrate outstanding classification accuracy with both fine-tuned linear probe and K-NN protocol across various datasets, showing competitiveness with existing state-of-the-art self-supervised learning methods. Moreover, we provide an end-to-end theoretical guarantee for downstream classification tasks in a misspecified, over-parameterized setting, highlighting how a large amount of unlabeled data contributes to prediction accuracy. Our theoretical findings suggest that the testing error of downstream tasks depends solely on the efficiency of data augmentation used in ACT when the unlabeled sample size is sufficiently large. This offers a theoretical understanding of learning downstream tasks with a small sample size.

本研究解决了在无标签场景下进行数据表示学习的挑战，提出了一种基于对抗对比训练的新型无监督迁移学习方法。实验结果显示，该方法在多个数据集上的分类准确性优于现有的自监督学习方法，并为下游分类任务提供了理论保障，强调了大量无标签数据对预测准确性的贡献。

通过对抗对比训练进行无监督迁移学习