ToCoAD：面向工业异常检测的两阶段对比学习
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection
AI总结：
本文提出两阶段对比学习策略ToCoAD，先以自监督方式训练判别网络，再通过自举对比学习引导特征提取器逐步学习工业异常分布，在多个工业异常检测基准上取得领先性能。
摘要：
当前的无监督异常检测方法在公开数据集上表现良好，但由于预训练特征提取器与目标特定领域之间的领域差距，难以应对特定类型的异常。为解决这一问题，本文提出了一种两阶段训练策略，称为ToCoAD。在第一阶段，以自监督学习方式利用合成异常训练一个判别网络。随后在第二阶段利用该网络提供负特征引导，通过自举对比学习辅助特征提取器的训练。该方法使模型能够逐步学习工业数据集中特有的异常分布，有效增强其对各类异常的泛化能力。大量实验验证了所提两阶段训练策略的有效性，模型取得了具有竞争力的性能，在MVTec AD、VisA和BTAD上分别实现了98.21%、98.43%和97.70%的像素级AUROC分数。
英文摘要：
Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extractors and target-specific domains. To tackle this issue, this paper presents a two-stage training strategy, called \textbf{ToCoAD}. In the first stage, a discriminative network is trained by using synthetic anomalies in a self-supervised learning manner. This network is then utilized in the second stage to provide a negative feature guide, aiding in the training of the feature extractor through bootstrap contrastive learning. This approach enables the model to progressively learn the distribution of anomalies specific to industrial datasets, effectively enhancing its generalizability to various types of anomalies. Extensive experiments are conducted to demonstrate the effectiveness of our proposed two-stage training strategy, and our model produces competitive performance, achieving pixel-level AUROC scores of 98.21\%, 98.43\% and 97.70\% on MVTec AD, VisA and BTAD respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
