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Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class.
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This foundational few-shot learning method was tested on mini-ImageNet (natural photos). We test whether the learned metric space transfers to Sentinel-2 satellite imagery for land cover classification — a fundamentally different visual domain where rare habitat types have very few labeled examples.
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Anne Fouilloux
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