Learning Placeholders for Open-Set Recognition

• URL: http://arxiv.org/abs/2103.15086v1
• Date: Sun, 28 Mar 2021 09:18:15 GMT
• Title: Learning Placeholders for Open-Set Recognition
• Authors: Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
• Abstract summary: We propose PlaceholdeRs for Open-SEt Recognition (Proser) to maintain classification performance on known classes and reject unknowns. Proser efficiently generates novel class by manifold mixup, and adaptively sets the value of reserved open-set classifier during training.
• Score: 38.57786747665563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model will recognize them as known ones. Under such circumstances, open-set recognition is proposed to maintain classification performance on known classes and reject unknowns. The closed-set models make overconfident predictions over familiar known class instances, so that calibration and thresholding across categories become essential issues when extending to an open-set environment. To this end, we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which prepares for the unknown classes by allocating placeholders for both data and classifier. In detail, learning data placeholders tries to anticipate open-set class data, thus transforms closed-set training into open-set training. Besides, to learn the invariant information between target and non-target classes, we reserve classifier placeholders as the class-specific boundary between known and unknown. The proposed Proser efficiently generates novel class by manifold mixup, and adaptively sets the value of reserved open-set classifier during training. Experiments on various datasets validate the effectiveness of our proposed method.

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