2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-2.7
Paper Title OPEN-SET DOMAIN GENERALIZATION VIA METRIC LEARNING
Authors Kai Katsumata, Ikki Kishida, University of Tokyo, Japan; Ayako Amma, Woven Planet Holdings, Inc., Japan; Hideki Nakayama, University of Tokyo, Japan
SessionMLR-APPL-IVSMR-2: Machine learning for image and video sensing, modeling and representation 2
LocationArea D
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
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Abstract In this study, we address open-set domain generalization, which aims to reject unknown class samples while classifying known class samples in unseen domains. Conventional domain generalization has the problem of unknown class samples being classified as known classes because domain generalization methods align feature distributions without distinction between known and unknown classes. To tackle this problem, we propose a decoupling loss that diffuses the feature representations of unknown samples. The loss allows us to construct a feature space that can better distinguish unknown samples. We demonstrate the effectiveness of decoupling loss using open-set domain generalization benchmarks.