  Data shift robustness has been primarily investigated from a fully supervised
perspective, and robustness of zero-shot learning (ZSL) models have been
largely neglected. In this paper, we present novel analyses on the robustness
of discriminative ZSL to image corruptions. We subject several ZSL models to a
large set of common corruptions and defenses. In order to realize the
corruption analysis, we curate and release the first ZSL corruption robustness
datasets SUN-C, CUB-C and AWA2-C. We analyse our results by taking into account
the dataset characteristics, class imbalance, class transitions between seen
and unseen classes and the discrepancies between ZSL and GZSL performances. Our
results show that discriminative ZSL suffers from corruptions and this trend is
further exacerbated by the severe class imbalance and model weakness inherent
in ZSL methods. We then combine our findings with those based on adversarial
attacks in ZSL, and highlight the different effects of corruptions and
adversarial examples, such as the pseudo-robustness effect present under
adversarial attacks. We also obtain new strong baselines for both models with
the defense methods. Finally, our experiments show that although existing
methods to improve robustness somewhat work for ZSL models, they do not produce
a tangible effect.
