Anomalous Samples for Few-Shot Anomaly Detection

• URL: http://arxiv.org/abs/2507.23712v1
• Date: Thu, 31 Jul 2025 16:41:06 GMT
• Title: Anomalous Samples for Few-Shot Anomaly Detection
• Authors: Aymane Abdali, Bartosz Boguslawski, Lucas Drumetz, Vincent Gripon,
• Abstract summary: We propose a methodology that incorporates anomalous samples in a multi-score anomaly detection score.<n>We compare the utility of anomalous samples to that of regular samples and study the benefits and limitations of each.
• Score: 5.199807441687141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several anomaly detection and classification methods rely on large amounts of non-anomalous or "normal" samples under the assump- tion that anomalous data is typically harder to acquire. This hypothesis becomes questionable in Few-Shot settings, where as little as one anno- tated sample can make a significant difference. In this paper, we tackle the question of utilizing anomalous samples in training a model for bi- nary anomaly classification. We propose a methodology that incorporates anomalous samples in a multi-score anomaly detection score leveraging recent Zero-Shot and memory-based techniques. We compare the utility of anomalous samples to that of regular samples and study the benefits and limitations of each. In addition, we propose an augmentation-based validation technique to optimize the aggregation of the different anomaly scores and demonstrate its effectiveness on popular industrial anomaly detection datasets.

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