This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://github.com/IIT-PAVIS/LearnAug-UDA

翻译：本文提出一种基于可学习数据增强的分类框架，以解决单样本无监督域适应（OS-UDA）问题。OS-UDA是域适应中最具挑战性的设定，因为其假设仅有一个未标记的目标样本可用于模型适配。受该单一样本驱动，我们的方法LearnAug-UDA学习如何增强源域数据，使其在感知上与目标域相似。由此，在增强数据上训练的分类器将能很好地泛化至目标域。为实现此目标，我们设计了一种编码器-解码器架构，该架构利用感知损失和风格迁移策略来增强源域数据。我们的方法在DomainNet和VisDA这两个著名的域适应基准测试上取得了最先进的性能。项目代码可在https://github.com/IIT-PAVIS/LearnAug-UDA获取。