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Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery.
[PURPOSE] A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is cruci…