Synergetic proto-pull and reciprocal points for open set recognition

Open set recognition (OSR) aims to accept and classify known classes while rejecting unknown classes, which is the key technology for pattern recognition algorithms to be widely applied in practice. The challenges to OSR is to reduce the empirical classification risk of known classes and the open space risk of potential unknown classes. However, the existing OSR methods less consider to optimize the open space risk, and much dark information in unknown space is not taken into account, which results in that many unknown classes are misidentified as known classes. Therefore, we present a self-supervised learningbased OSR method with synergetic proto-pull and reciprocal points, which can remarkably reduce the risks of empirical classification and open space. Especially, we propose a new concept of proto-pull point, which can be synergistically combined with reciprocal points to shrink the feature spaces of known and unknown classes, and increase the feature distance between different classes, so as to form a good feature distribution. In addition, a self-supervised learning task of identifying the directions of rotated images is introduced in OSR model training, which is benefit for the OSR mdoel to capture more distinguishing features, and decreases both empirical classification and open space risks. The final experimental results on benchmark datasets show that our propsoed approach outperforms most existing OSR methods.