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Random Word Data Augmentation for Zero-Shot Anomaly Detection

This paper presents a novel method that leverages a visual-language model, CLIP, as a data source for zero-shot anomaly detection.

Data and Resources

Cite this as

Masato Tamura (2024). Dataset: Random Word Data Augmentation for Zero-Shot Anomaly Detection. https://doi.org/10.57702/76mdjvra

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2308.11119
Author Masato Tamura