COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

• URL: http://arxiv.org/abs/2402.18998v1
• Date: Thu, 29 Feb 2024 09:48:19 GMT
• Title: COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
• Authors: Jingyi Liao, Xun Xu, Manh Cuong Nguyen, Adam Goodge, Chuan Sheng Foo
• Abstract summary: We propose a novel methodology to address the challenge of FSAD. We employ a model pre-trained on a large source dataset to model weights. We evaluate few-shot anomaly detection on on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method.
• Score: 19.946344683965425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method.

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