Dual Prototype Learning for Robust Open Set Recognition

Yini Wang, Xiaodong Yue, Zhikang Xu, Zihao Li

Abstract: Given a specific class space, open set recognition (OSR) requires a model to not only accurately classify data samples belonging to that class space, but also to identify samples from unseen classes. Based on prototype learning, existing OSR methods seek to build a separable feature space in which the unseen classes are as far away as possible from the given classes. However, these methods may overly focus on either the unseen classes or the given classes, resulting in unstable performance and lack of robustness. To handle this challenge, a robust OSR learning framework, named dual prototype learning is proposed. During the training phase, a set of dual prototypes is created for each given class, where one prototype (intra-class prototype) is used to form a compact intra-class feature representation, and the other prototype (anti-class prototype) is used to form a space that does not belong to the class by pushing the anti-class prototype away from the intra-class prototype. In the inference phase, our method can achieve a more stable and robust decision-making process by simultaneously measuring the distance between the sample and the intra-class prototypes, as well as the distance between the sample and the anti-class prototypes. Qualitative quantitative experiments demonstrate that the proposed method outperforms existing methods in terms of classification performance and robustness.