  Zero-shot learning (ZSL) aims to recognize novel classes by transferring
semantic knowledge from seen classes to unseen classes. Though many ZSL methods
rely on a direct mapping between the visual and the semantic space, the
calibration deviation and hubness problem limit the generalization capability
to unseen classes. Recently emerged generative ZSL methods generate unseen
image features to transform ZSL into a supervised classification problem.
However, most generative models still suffer from the seen-unseen bias problem
as only seen data is used for training. To address these issues, we propose a
novel bidirectional embedding based generative model with a tight
visual-semantic coupling constraint. We learn a unified latent space that
calibrates the embedded parametric distributions of both visual and semantic
spaces. Since the embedding from high-dimensional visual features comprise much
non-semantic information, the alignment of visual and semantic in latent space
would inevitably been deviated. Therefore, we introduce information bottleneck
(IB) constraint to ZSL for the first time to preserve essential attribute
information during the mapping. Specifically, we utilize the uncertainty
estimation and the wake-sleep procedure to alleviate the feature noises and
improve model abstraction capability. In addition, our method can be easily
extended to transductive ZSL setting by generating labels for unseen images. We
then introduce a robust loss to solve this label noise problem. Extensive
experimental results show that our method outperforms the state-of-the-art
methods in different ZSL settings on most benchmark datasets. The code will be
available at https://github.com/osierboy/IBZSL.
