  Generalized zero-shot learning (GZSL) aims to recognize samples from both
seen and unseen classes using only seen class samples for training. However,
GZSL methods are prone to bias towards seen classes during inference due to the
projection function being learned from seen classes. Most methods focus on
learning an accurate projection, but bias in the projection is inevitable. We
address this projection bias by proposing to learn a parameterized Mahalanobis
distance metric for robust inference. Our key insight is that the distance
computation during inference is critical, even with a biased projection. We
make two main contributions - (1) We extend the VAEGAN (Variational Autoencoder
\& Generative Adversarial Networks) architecture with two branches to
separately output the projection of samples from seen and unseen classes,
enabling more robust distance learning. (2) We introduce a novel loss function
to optimize the Mahalanobis distance representation and reduce projection bias.
Extensive experiments on four datasets show that our approach outperforms
state-of-the-art GZSL techniques with improvements of up to 3.5 \% on the
harmonic mean metric.
