  It is well-known that zero-shot learning (ZSL) can suffer severely from the
problem of domain shift, where the true and learned data distributions for the
unseen classes do not match. Although transductive ZSL (TZSL) attempts to
improve this by allowing the use of unlabelled examples from the unseen
classes, there is still a high level of distribution shift. We propose a novel
TZSL model (named as Bi-VAEGAN), which largely improves the shift by a
strengthened distribution alignment between the visual and auxiliary spaces.
The key proposal of the model design includes (1) a bi-directional distribution
alignment, (2) a simple but effective L_2-norm based feature normalization
approach, and (3) a more sophisticated unseen class prior estimation approach.
In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state
of the arts under both the standard and generalized TZSL settings. Code could
be found at https://github.com/Zhicaiwww/Bi-VAEGAN
