  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.
