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240313s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3374048
|2 doi
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|a pubmed24n1336.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Liao, Jingyi
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a COFT-AD
|b COntrastive Fine-Tuning for Few-Shot Anomaly Detection
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| 264 |
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 18.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Existing approaches towards anomaly detection (AD) often rely on a substantial amount of anomaly-free data to train representation and density models. However, large anomaly-free datasets may not always be available before the inference stage; in which case an anomaly detection model must be trained with only a handful of normal samples, a.k.a. few-shot anomaly detection (FSAD). In this paper, we propose a novel methodology to address the challenge of FSAD which incorporates two important techniques. Firstly, we employ a model pre-trained on a large source dataset to initialize model weights. Secondly, to ameliorate the covariate shift between source and target domains, we adopt contrastive training to fine-tune on the few-shot target domain data. To learn suitable representations for the downstream AD task, we additionally incorporate cross-instance positive pairs to encourage a tight cluster of the normal samples, and negative pairs for better separation between normal and synthesized negative samples. We evaluate few-shot anomaly detection on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method
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|a Journal Article
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| 700 |
1 |
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|a Xu, Xun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nguyen, Manh Cuong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Goodge, Adam
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Foo, Chuan Sheng
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 18., Seite 2090-2103
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:33
|g year:2024
|g day:18
|g pages:2090-2103
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2024.3374048
|3 Volltext
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|a AR
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| 952 |
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|d 33
|j 2024
|b 18
|h 2090-2103
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