This paper studies the problem of domain division which aims to segment instances drawn from different probabilistic distributions. This problem exists in many previous recognition tasks, such as Open Set Learning (OSL) and Generalized Zero-Shot Learning (G-ZSL), where the testing instances come from either seen or unseen/novel classes with different probabilistic distributions. Previous works only calibrate the conﬁdent prediction of classiﬁers of seen classes (WSVM Scheirer et al. (2014)) or taking unseen classes as outliers Socher et al. (2013). In contrast, this paper proposes a probabilistic way of directly estimating and ﬁne-tuning the decision boundary between seen and unseen classes. In particular, we propose a domain division algorithm to split the testing instances into known, unknown and uncertain domains, and then conduct recognition tasks in each domain. Two statistical tools, namely, bootstrapping and KolmogorovSmirnov (K-S) Test, for the ﬁrst time, are introduced to uncover and ﬁne-tune the decision boundary of each domain. Critically, the uncertain domain is newly introduced in our framework to adopt those instances whose domain labels cannot be predicted conﬁdently. Extensive experiments demonstrate that our approach achieved the state-of-the-art performance on OSL and G-ZSL benchmarks.
 This paper studies the problem of domain division by segmenting instances drawn from different probabilistic distributions.   This paper deals with the problem of novelty recognition in open set learning and generalized zero-shot learning and proposes a possible solution An approach to domain separation based on bootstrapping to identify similarity cutoff thresholds for known classes, followed by a Kolmogorov-Smirnoff test to refine the bootstrapped in-distribution zones. Proposes to introduce a new domain, the uncertain domain, to better handle the division between seen/unseen domains in open-set and generalized zero-shot learning