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Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One
Conventional semi-supervised learning (SSL) lies in the close-set assumption that the labeled and unlabeled sets contain data with the same seen classes, called in-distribution (ID) data.
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis
To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input.
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