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. Our codes can be accessed at github.com/kowshikthopalli/SISTA.
Original language | English (US) |
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Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
DOIs | |
State | Published - 2023 |
Event | 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: Jun 4 2023 → Jun 10 2023 |
Keywords
- augmentation
- domain adaptation
- GANs
- generalization
- single-shot learning
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering