Classification-Reconstruction Learning for Open-Set Recognition

CVPR 2019 Ryota YoshihashiWen ShaoRei KawakamiShaodi YouMakoto IidaTakeshi Naemura

Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns... (read more)

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