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Classification models trained on data from one source may underperform when tested on data acquired from different sources due to shifts in data distributions, which limit the models’ generalizability in real-world applications. Domain adaptation methods proposed to align such shifts in source-target data distributions use contrastive learning or adversarial techniques with or without internal cluster alignment. The intracluster alignment is performed using standalone k-means clustering on image embedding. This paper introduces a novel deep clustering approach to align cluster distributions in tandem with adapting source and target data distributions. Our method learns and aligns a mixture of cluster distributions in the unlabeled target domain with those in the source domain in a unified deep representation learning framework. Experiments demonstrate that intra-cluster alignment improves classification accuracy in nine out of ten domain adaptation examples. These improvements range between 0.3% and 2.0% compared to k-means clustering of embedding and between 0.4% and 5.8% compared to methods without class-level alignment. Unlike current domain adaptation methods, the proposed cluster distribution-based deep learning provides a quantitative and explainable measure of distribution shifts in data domains. We have publicly shared the source code for the algorithm implementation.
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Single-Shot Domain Adaptation via Target-Aware Generative Augmentations
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.
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- Award ID(s):
- 1828010
- PAR ID:
- 10432749
- Date Published:
- Journal Name:
- ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP49357.2023.10096784
