The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic data augmentations in cases of limited target data availability. In this paper, we consider the challenging setting of single-shot adaptation and explore the design of augmentation strategies. We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts, and hence propose a new approach SiSTA (Single-Shot Target Augmentations), which first fine-tunes a generative model from the source domain using a single-shot target, and then employs novel sampling strategies for curating synthetic target data. Using experiments with a state-of-the-art domain adaptation method, we find that SiSTA produces improvements as high as 20\% over existing baselines under challenging shifts in face attribute detection, and that it performs competitively to oracle models obtained by training on a larger target dataset.

翻译：利用来自任何目标领域的数据从源领域调整模型的问题由于深层神经网络的简单化而变得日益突出。虽然出现了几种试验时间适应技术,但它们通常在目标数据有限的情况下依靠合成数据增强。在本文中,我们考虑了单发适应的艰难设置,并探索了增强战略的设计。我们争辩说,现有方法使用的增强功能不足以应对大规模分布转移,因此提出了一种新的方法SiSTA(Single-Shot目标增强功能),该方法首先用单发目标从源领域微调一种基因化模型,然后采用新的取样战略来整理合成目标数据。我们发现,SiSTA利用最新领域适应方法的实验,在面部属性检测方面有挑战性转变的现有基线的基础上,取得了高达20英寸的改进,并且它以竞争方式与通过对大目标数据集的培训而获得的模型进行交接。