Class-Specific Semantic Generation and Reconstruction Learning for Open Set Recognition

Open set recognition is a crucial research theme for open-environment machine learning. For this problem, a common solution is to learn compact representations of known classes and identify unknown samples by measuring deviations from these known classes. However, the aforementioned methods (1) lack open training consideration, which is detrimental to the fitting of known classes, and (2) recognize unknown classes on an inadequate basis, which limits the accuracy of recognition. In this study, we propose an open reconstruction learning framework that learns a union boundary region of known classes to characterize unknown space. This facilitates the isolation of known space from unknown space to represent known classes compactly and provides a more reliable recognition basis from the perspective of both known and unknown space. Specifically, an adversarial constraint is used to generate class-specific boundary samples. Then, the known classes and approximate unknown space are fitted with manifolds represented by class-specific auto-encoders. Finally, the auto-encoders output the reconstruction error in terms of known and unknown spaces to recognize samples. Extensive experimental results show that the proposed method outperforms existing advanced methods and achieves new stateof-the-art performance. The code is available at https://github.com/Ashowman98/CSGRL.