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Single-Shot Domain Adaptation via Target-Aware Generative Augmentation

2022-10-29 20:53:57
Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan Turaga, Jayaraman J. Thiagarajan

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.16692

PDF

https://arxiv.org/pdf/2210.16692.pdf


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