Target-Aware Generative Augmentations for Single-Shot Adaptation
- URL: http://arxiv.org/abs/2305.13284v1
- Date: Mon, 22 May 2023 17:46:26 GMT
- Title: Target-Aware Generative Augmentations for Single-Shot Adaptation
- Authors: Kowshik Thopalli, Rakshith Subramanyam, Pavan Turaga and Jayaraman J.
Thiagarajan
- Abstract summary: We propose a new approach to adapting models from a source domain to a target domain.
SiSTA 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.
We find that SiSTA produces significantly improved generalization over existing baselines in face detection and multi-class object recognition.
- Score: 21.840653627684855
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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