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Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?

, , , , and . EUSIPCO, page 1283-1287. IEEE, (2022)

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Random subspaces NMF for unsupervised transfer learning., and . IJCNN, page 3901-3908. IEEE, (2014)Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation., and . NeurIPS, page 7408-7417. (2018)Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?, , , , and . EUSIPCO, page 1283-1287. IEEE, (2022)Margin-aware Adversarial Domain Adaptation with Optimal Transport., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2514-2524. PMLR, (2020)Theoretical Analysis of Domain Adaptation with Optimal Transport., , and . CoRR, (2016)Sparsity analysis of learned factors in Multilayer NMF., and . IJCNN, page 1-7. IEEE, (2015)Controlling orthogonality constraints for better NMF clustering., and . IJCNN, page 3894-3900. IEEE, (2014)Theoretical Analysis of Domain Adaptation with Optimal Transport., , and . ECML/PKDD (2), volume 10535 of Lecture Notes in Computer Science, page 737-753. Springer, (2017)Optimal Transport for Multi-source Domain Adaptation under Target Shift., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 849-858. PMLR, (2019)Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport., , and . ECML/PKDD (2), volume 11052 of Lecture Notes in Computer Science, page 759-776. Springer, (2018)