Jan 2, 2023 · We review the emerging field of equivariant imaging (EI) and show how it can provide improved generalization and new imaging opportunities.
Jan 2, 2023 · Symmetry can also be incorporated into deep neural networks (DNNs) in the form of equivariance, allowing for more data-efficient learning. While ...
Dec 29, 2022 · The first method, motivated by unrolled optimization algorithms, relies on building equivariant proximal subnetworks within the reconstruction ...
Sep 7, 2022 · In the next two sections, we review ways to remedy this through either a modified unrolled network architecture or through the training process ...
The emerging field of equivariant imaging (EI) is reviewed and it is shown how it can provide improved generalization and new imaging opportunities and the ...
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: ...
Jan 9, 2023 · Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning. Dongdong Chen, Mike Davies, Matthias J ...
Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning. January 2023 · IEEE Signal Processing Magazine. Dongdong ...
Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning. Dongdong Chen (1) , Mike Davies (2) , Matthias Ehrhardt ...
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Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning. Dongdong Chen, Mike Davies, Matthias J. Ehrhardt, Carola ...