Methods › General › Domain Adaptation › SIFA
Synergistic Image and Feature Alignment
SIFA
Introduced by Cheng Chen et al. in Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Synergistic Image and Feature Alignment is an unsupervised domain adaptation framework that conducts synergistic alignment of domains from both image and feature perspectives. In SIFA, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning.
Papers archive 2025-07-28
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Stand-Alone Inter-Frame Attention in Video Models 14 Jun 2022 · 1 repository · arXiv:2206.06931
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Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation 6 Feb 2020 · 1 repository · arXiv:2002.02255
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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