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A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to achieve good performance in spatially equivariant tasks (e.g.
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Disentangled representation learning in cardiac image analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou, Scott Semple, Michelle Williams, David E. Newby, Rohan Dharmakumar, and Sotirios A. Tsaftaris · 2019
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Unsupervised robust disentangling of latent characteristics for image synthesis
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Emergence of invariance and disentanglement in deep representations
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Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
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