  Open set recognition is an emerging research area that aims to simultaneously
classify samples from predefined classes and identify the rest as 'unknown'. In
this process, one of the key challenges is to reduce the risk of generalizing
the inherent characteristics of numerous unknown samples learned from a small
amount of known data. In this paper, we propose a new concept, Reciprocal
Point, which is the potential representation of the extra-class space
corresponding to each known category. The sample can be classified to known or
unknown by the otherness with reciprocal points. To tackle the open set
problem, we offer a novel open space risk regularization term. Based on the
bounded space constructed by reciprocal points, the risk of unknown is reduced
through multi-category interaction. The novel learning framework called
Reciprocal Point Learning (RPL), which can indirectly introduce the unknown
information into the learner with only known classes, so as to learn more
compact and discriminative representations. Moreover, we further construct a
new large-scale challenging aircraft dataset for open set recognition: Aircraft
300 (Air-300). Extensive experiments on multiple benchmark datasets indicate
that our framework is significantly superior to other existing approaches and
achieves state-of-the-art performance on standard open set benchmarks.
