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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation

February 1, 2023

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Authors

Kaihong Wang

Boston University


Chenhongyi Yang

Boston University


Margrit Betke

Boston University


DOI:

10.1609/aaai.v35i11.17216


Abstract:

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes. Code and pretrained style transfer model are available at: https://github.com/wangkaihong/BiSIDA.

Topics: AAAI

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HOW TO CITE:

Kaihong Wang||Chenhongyi Yang||Margrit Betke Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation AAAI 2021, 10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke (2021). Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. 2021. Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. (2021) "Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.10138-10146

Kaihong Wang||Chenhongyi Yang||Margrit Betke, "Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation", AAAI, p.10138-10146, 2021.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. "Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. "Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 10138-10146.

Kaihong Wang||Chenhongyi Yang||Margrit Betke. Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation. AAAI[Internet]. 2021[cited 2023]; 10138-10146.


ISSN: 2374-3468


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