Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in
  Open Worlds
Tianqi Li Guansong Pang Xiao Bai Jin Zheng Lei Zhou Xin Ning
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes
with only their side semantic information presented during training. It cannot
handle real-life, open-world scenarios where there are test samples of unknown
classes for which neither samples (e.g., images) nor their side semantic
information is known during training. Open-Set Recognition (OSR) is dedicated
to addressing the unknown class issue, but existing OSR methods are not
designed to model the semantic information of the unseen classes. To tackle
this combined ZSL and OSR problem, we consider the case of "Zero-Shot Open-Set
Recognition" (ZS-OSR), where a model is trained under the ZSL setting but it is
required to accurately classify samples from the unseen classes while being
able to reject samples from the unknown classes during inference. We perform
large experiments on combining existing state-of-the-art ZSL and OSR models for
the ZS-OSR task on four widely used datasets adapted from the ZSL task, and
reveal that ZS-OSR is a non-trivial task as the simply combined solutions
perform badly in distinguishing the unseen-class and unknown-class samples. We
further introduce a novel approach specifically designed for ZS-OSR, in which
our model learns to generate adversarial semantic embeddings of the unknown
classes to train an unknowns-informed ZS-OSR classifier. Extensive empirical
results show that our method 1) substantially outperforms the combined
solutions in detecting the unknown classes while retaining the classification
accuracy on the unseen classes and 2) achieves similar superiority under
generalized ZS-OSR settings.