{"ID":2888465,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23712","arxiv_id":"2507.23712","title":"Anomalous Samples for Few-Shot Anomaly Detection","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.","short_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 differenc...","url_abs":"https://arxiv.org/abs/2507.23712","url_pdf":"https://arxiv.org/pdf/2507.23712v1","authors":"[\"Aymane Abdali\",\"Bartosz Boguslawski\",\"Lucas Drumetz\",\"Vincent Gripon\"]","published":"2025-07-31T16:41:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}