Classification-Reconstruction Learning for Open-Set Recognition
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by
Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida,
Takeshi Naemura
2018
Abstract
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. In contrast, we train networks for joint classification and
reconstruction of input data. This enhances the learned representation so as to
preserve information useful for separating unknowns from knowns, as well as to
discriminate classes of knowns. Our novel Classification-Reconstruction
learning for Open-Set Recognition (CROSR) utilizes latent representations for
reconstruction and enables robust unknown detection without harming the
known-class classification accuracy. Extensive experiments reveal that the
proposed method outperforms existing deep open-set classifiers in multiple
standard datasets and is robust to diverse outliers.
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