Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.
From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
1 Pith paper cite this work. Polarity classification is still indexing.
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Learning Color Equivariant Representations
Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.