  Generalized Zero-Shot Learning (GZSL) is a challenging topic that has
promising prospects in many realistic scenarios. Using a gating mechanism that
discriminates the unseen samples from the seen samples can decompose the GZSL
problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised
classification problem. However, training the gate is usually challenging due
to the lack of data in the unseen domain. To resolve this problem, in this
paper, we propose a boundary based Out-of-Distribution (OOD) classifier which
classifies the unseen and seen domains by only using seen samples for training.
First, we learn a shared latent space on a unit hyper-sphere where the latent
distributions of visual features and semantic attributes are aligned
class-wisely. Then we find the boundary and the center of the manifold for each
class. By leveraging the class centers and boundaries, the unseen samples can
be separated from the seen samples. After that, we use two experts to classify
the seen and unseen samples separately. We extensively validate our approach on
five popular benchmark datasets including AWA1, AWA2, CUB, FLO and SUN. The
experimental results demonstrate the advantages of our approach over
state-of-the-art methods.
