Abstract
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 Generative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the proposed 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.
Original language | English |
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Title of host publication | British Machine Vision Conference Proceedings 2017 |
Editors | Krystian Mikolajczyk, Gabriel Brostow |
Place of Publication | London UK |
Publisher | British Machine Vision Association and Society for Pattern Recognition |
Number of pages | 12 |
ISBN (Electronic) | 9781901725605 |
ISBN (Print) | 190172560X |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | British Machine Vision Conference 2017 - Royal Geographic Society of London, London, United Kingdom Duration: 4 Sep 2017 → 7 Sep 2017 Conference number: 28th https://bmvc2017.london/ https://dblp.org/db/conf/bmvc/bmvc2017.html |
Publication series
Name | British Machine Vision Conference 2017, BMVC 2017 |
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Conference
Conference | British Machine Vision Conference 2017 |
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Abbreviated title | BMVC 2017 |
Country | United Kingdom |
City | London |
Period | 4/09/17 → 7/09/17 |
Internet address |