论文标题
DSR-表面异常检测的双重子空间重新投影网络
DSR -- A dual subspace re-projection network for surface anomaly detection
论文作者
论文摘要
歧视性无监督的表面异常检测的最先进的依赖于外部数据集用于合成异常训练图像的外部数据集。这种方法很容易出现近乎分布异常的失败，因为由于它们与无异常区域的相似性，因此很难现实地合成这些异常。我们提出了一个基于双重解码器DSR的量化特征空间表示的体系结构，该体系结构避免了图像级异常合成要求。在没有对异常的视觉属性进行任何假设的情况下，DSR通过对学到的量化特征空间进行采样，从而在特征级别生成异常，从而允许受控生成近乎分布的异常。 DSR在KSDD2和MVTEC异常检测数据集上实现了最新结果。关于具有挑战性的现实世界KSDD2数据集的实验表明，DSR显着超过其他无监督的表面异常检测方法，在异常检测中提高了10％的AP，在异常定位中将前表现最好的AP提高了10％。
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are difficult to be synthesized realistically due to their similarity to anomaly-free regions. We propose an architecture based on quantized feature space representation with dual decoders, DSR, that avoids the image-level anomaly synthesis requirement. Without making any assumptions about the visual properties of anomalies, DSR generates the anomalies at the feature level by sampling the learned quantized feature space, which allows a controlled generation of near-in-distribution anomalies. DSR achieves state-of-the-art results on the KSDD2 and MVTec anomaly detection datasets. The experiments on the challenging real-world KSDD2 dataset show that DSR significantly outperforms other unsupervised surface anomaly detection methods, improving the previous top-performing methods by 10% AP in anomaly detection and 35% AP in anomaly localization.
