Abstract
The open-set recognition task is proposed to handle unknown classes that do not belong to any of the classes in training set. The methods should reject unknown samples while maintaining high classification accuracy on the known classes. Previous methods are divided into two stages, including open-set identification and closed-set classification. These methods usually reject unknown samples according to the previous analysis of the known classes. However, this would inevitably cause risks if the discriminative representation from the unknown classes is insufficient. In contrast to the previous methods, we propose a new method which uses a dual probability distribution to represent the unknowns. From the dual distribution, the boundary of known space is naturally derived, thereby helping identify the unknowns without staging or thresholding. Following this formulation, this paper proposed a new method called Dual Probability Learning Model (DPLM). The model built a neural Gaussian Mixed Model for probability estimation. To learn this model, we also added the normalized joint probability of latent representations into the objective function in the training stage. The results showed that the proposed method is highly effective.
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Liu, S., Yang, F. (2021). Open-Set Recognition with Dual Probability Learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_11
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