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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Improved Losses for Open-Set Classification |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Open-Set classification (OSC) addresses one of the core issues of traditional classification techniques, namely, the underlying closed-world assumption. The goal of OSC methods is to classify known classes correctly while also rejecting unknown classes. We propose two novel generic loss functions, Margin-OS and Margin-EOS, which combine the Entropic Open-Set and Objectosphere loss with margin-based loss functions used in face recognition tasks, CosFace and ArcFace, to learn discriminative features. We find that the margin has a positive effect on the closed-set accuracy but a mixed effect on the open-set performance. For applications that can tolerate high false positive rates, our losses improve the classification of known classes, but for low false positive rates the margin negatively impacts the training which leads to subpar classification of known samples. |
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