Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data

• URL: http://arxiv.org/abs/2310.10461v2
• Date: Fri, 9 Feb 2024 16:59:43 GMT
• Title: Model Selection of Zero-shot Anomaly Detectors in the Absence of Labeled Validation Data
• Authors: Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph
• Abstract summary: Anomaly detection requires detecting abnormal samples in large unlabeled datasets. We propose SWSA: a framework to select image-based anomaly detectors with a generated synthetic validation set. We find that SWSA often selects models that match selections made with a ground-truth validation set, resulting in higher AUROCs than baseline methods.
• Score: 19.919234682696306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection requires detecting abnormal samples in large unlabeled datasets. While progress in deep learning and the advent of foundation models has produced powerful zero-shot anomaly detection methods, their deployment in practice is often hindered by the lack of labeled data -- without it, their detection performance cannot be evaluated reliably. In this work, we propose SWSA (Selection With Synthetic Anomalies): a general-purpose framework to select image-based anomaly detectors with a generated synthetic validation set. Our proposed anomaly generation method assumes access to only a small support set of normal images and requires no training or fine-tuning. Once generated, our synthetic validation set is used to create detection tasks that compose a validation framework for model selection. In an empirical study, we find that SWSA often selects models that match selections made with a ground-truth validation set, resulting in higher AUROCs than baseline methods. We also find that SWSA selects prompts for CLIP-based anomaly detection that outperform baseline prompt selection strategies on all datasets, including the challenging MVTec-AD and VisA datasets.

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