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 (W-SVM 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 describes 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. The authors apply these techniques to two general recognition problems: open-world recognition, and Generalized Zero-shot Learning (GZSL). Experimental results are given on a variety of datasets, and a thorough comparative evaluation on GZSL is performed.
The paper has the following strong points:
 1. The motivations for each element of the proposed approaches are fairly well presented and are compelling.
 
 2. The experimental results on GZSL are impressive, especially compared to established approaches.
The paper also has the following weak points:
 1. The writing is a bit rough throughout, though not to extreme distraction. The abstract starts right off with the awkward "This paper studies the problem of domain division problem..." The manuscript needs more careful revision for clarity.
 2. Related to the previous point, there are several elements of the technical exposition that are lacking in clarity. For example, it is unclear what eq. 9 is defining exactly. It seems to be the final decision rule, but it is not clear how seen, unseen, and uncertain samples are determined. Algorithm 1 is clear, but there is never a clear, complete definition of the end-to-end decision pipeline given. I feel like it would be difficult to reproduce the results of this article without significant trial-and-error.
 3. The authors seems to have relied on the Xian et al. paper to extract data for their comparative evaluation on GZSL. There are more recent works from 2018 that should be included, such as:
 Arora, Gundeep, Vinay Kumar Verma, Ashish Mishra and Piyush Rai. “Generalized Zero-Shot Learning via Synthesized Examples." CVPR 2018.
In summary, this paper has many interesting ideas, and the GZSL results are indeed impressive (with respect to the results in the comparison). However, there are many problems with clarity and missing, recent work in the comparative evaluation.