How Robust are Discriminatively Trained Zero-Shot Learning Models?
This work addresses the neglected problem of data shift robustness for zero-shot learning models, which is incremental as it extends robustness analysis from supervised to zero-shot settings.
The paper investigates the robustness of discriminatively trained zero-shot learning models to image corruptions, finding that they suffer significantly from corruptions, with performance degradation exacerbated by class imbalance and model weaknesses, and that existing robustness improvement methods have limited tangible effect.
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.