  This paper studies the problem of Generalized Zero-shot Learning (G-ZSL),
whose goal is to classify instances belonging to both seen and unseen classes
at the test time. We propose a novel space decomposition method to solve G-ZSL.
Some previous models with space decomposition operations only calibrate the
confident prediction of source classes (W-SVM [46]) or take target-class
instances as outliers [49]. In contrast, we propose to directly estimate and
fine-tune the decision boundary between the source and the target classes.
Specifically, we put forward a framework that enables to learn compositional
spaces by splitting the instances into Source, Target, and Uncertain spaces and
perform recognition in each space, where the uncertain space contains instances
whose labels cannot be confidently predicted. We use two statistical tools,
namely, bootstrapping and Kolmogorov-Smirnov (K-S) Test, to learn the
compositional spaces for G-ZSL. We validate our method extensively on multiple
G-ZSL benchmarks, on which it achieves state-of-the-art performances.
