Single-Shot Domain Adaptation via Target-Aware Generative Augmentation

• URL: http://arxiv.org/abs/2210.16692v1
• Date: Sat, 29 Oct 2022 20:53:57 GMT
• Title: Single-Shot Domain Adaptation via Target-Aware Generative Augmentation
• Authors: Rakshith Subramanyam, Kowshik Thopalli, Spring Berman, Pavan Turaga, Jayaraman J. Thiagarajan
• Abstract summary: We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts. We propose SiSTA (Single-Shot Target Augmentations), which first fine-tunes a generative model from the source domain using a single-shot target. We find that SiSTA produces improvements as high as 20% over existing baselines under challenging shifts in face detection.
• Score: 21.17396588958938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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