[MICCAI2019]Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation
MICCAI2019 多模分割相關論文筆記 Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation 深度無監督域自適應（UDA）旨在僅使用未標記的目標域數據和標記的源域數據來改善目標域上的深度神經網絡模型的性能。作者爲多域醫學圖像分割引入了一種新的數據有效的 UDA 方法。所提出的方法結合了
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相關文章
- 1. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 2. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
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- 5. 論文分享 Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations wi
- 6. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 7. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》論文筆記
- 8. 論文閱讀：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 9. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 10. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations