Abstract
In open set recognition (OSR), the model not only needs to correctly recognize known class samples, but also needs to be able to effectively reject unknown samples. To address this problem, we propose a joint learning model with post-processing based on the concept of Reciprocal Points. Specifically, to guarantee the accuracy of known class recognition, we design a two-branch network containing a self-supervised branch and a classification branch. The self-supervised branch helps the model classify known classes more accurately. Then, to avoid misjudging unknown samples as known ones with high confidence, we carefully redesign the open loss to better separate the known and unknown spaces, and design a post-processing mechanism to penalize the predictions of potential unknown samples. We perform several experiments and ablations on our model, obtaining the state-of-the-art results on most datasets for open set recognition and unknown detection tasks.
This research is supported by National Natural Science Foundation of China (Grant No. 61972271) and Sichuan Science and Technology Program (No. 2022YFS0557).
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Li, Q., Xing, G., Liu, Y. (2023). A Joint Learning Model for Open Set Recognition with Post-processing. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_35
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