Abstract

Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as unknown, is essential for reliable machine learning. The key challenge of OSR is how to reduce the empirical classification risk on the labeled known data and the open space risk on the potential unknown data simultaneously.

To handle the challenge, we formulate the open space risk problem from the perspective of multi-class integration, and model the unexploited extra-class space with a novel concept Reciprocal Point. Follow this, a novel Adversarial Reciprocal Point Learning framework is proposed to minimize the overlap of known distribution and unknown distributions without loss of known classification accuracy. Specifically, each reciprocal point is learned by the extra-class space with the corresponding known category, and the confrontation among multiple known categories are employed to reduce the empirical classification risk.

An adversarial margin constraint is proposed to reduce the open space risk by limiting the latent open space constructed by reciprocal points. Moreover, an instantiated adversarial enhancement method is designed to generate diverse and confusing training samples. Extensive experimental results on various benchmark datasets indicate that the proposed method is significantly superior to existing approaches and achieves state-of-the-art performance.