Generative OpenMax for Multi-Class Open Set Classification [article]

ZongYuan Ge, Sergey Demyanov, Zetao Chen, Rahil Garnavi
<span title="2017-07-24">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provide explicit modelling and decision score for unknown classes. The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the
more &raquo; ... ed method on two datasets of handwritten digits and characters, resulting in superior results over previous deep learning based method OpenMax Moreover, G-OpenMax provides a way to visualize samples representing the unknown classes from open space. Our simple and effective approach could serve as a new direction to tackle the challenging multi-class open set classification problem.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1707.07418v1</a> <a target="_blank" rel="external noopener" href="">fatcat:hdd6satvqne77glzyqgdebcr4e</a> </span>
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