cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks.
Saliva-based detection of covid-19 infection in a real-world setting us- ing reagent-free raman spectroscopy and machine learning
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Mind the Gap: Learning Modality-Agnostic Representations with a Cross-Modality UNet
cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks.