Target-Aware Generative Augmentations for Single-Shot Adaptation
Kowshik Thopalli Rakshith Subramanyam Pavan Turaga Jayaraman J. Thiagarajan
In this paper, we address the problem of adapting models from a source domain
to a target domain, a task that has become increasingly important due to the
brittle generalization of deep neural networks. While several test-time
adaptation techniques have emerged, they typically rely on synthetic toolbox
data augmentations in cases of limited target data availability. 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, 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 on a variety of
benchmarks, distribution shifts and image corruptions, we find that SiSTA
produces significantly improved generalization over existing baselines in face
attribute detection and multi-class object recognition. Furthermore, SiSTA
performs competitively to models obtained by training on larger target
datasets. Our codes can be accessed at https://github.com/Rakshith-2905/SiSTA.