Adversarial Vulnerabilities in Zero-Shot Learning: An Empirical Examination
Research#Zero-shot🔬 Research|Analyzed: Jan 10, 2026 09:01•
Published: Dec 21, 2025 08:55
•1 min read
•ArXivAnalysis
This ArXiv article examines the robustness of zero-shot learning models against adversarial attacks, a critical area for ensuring model reliability and safety. The empirical study likely provides valuable insights into the vulnerabilities of these models and potential mitigation strategies.
Key Takeaways
- •Identifies vulnerabilities in zero-shot learning models under adversarial attacks.
- •Investigates the impact of attacks at both class and concept levels.
- •Likely offers insights for improving the robustness of zero-shot learning systems.
Reference / Citation
View Original"The study focuses on vulnerabilities at the class and concept levels."