Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

• URL: http://arxiv.org/abs/2208.07769v1
• Date: Tue, 16 Aug 2022 14:29:15 GMT
• Title: Unsupervised Domain Adaptation for Segmentation with Black-box Source Model
• Authors: Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
• Abstract summary: We propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. We evaluate our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.
• Score: 37.02365343894657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.

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