We introduce a simple and intuitive self-supervision task, Natural Synthetic
Anomalies (NSA), for training an end-to-end model for anomaly detection and
localization using only normal training data. NSA integrates Poisson image
editing to seamlessly blend scaled patches of various sizes from separate
images. This creates a wide range of synthetic anomalies which are more similar
to natural sub-image irregularities than previous data-augmentation strategies
for self-supervised anomaly detection. We evaluate the proposed method using
natural and medical images. Our experiments with the MVTec AD dataset show that
a model trained to localize NSA anomalies generalizes well to detecting
real-world a priori unknown types of manufacturing defects. Our method achieves
an overall detection AUROC of 97.2 outperforming all previous methods that
learn without the use of additional datasets. Code available at
this https URL

通过自监督学习中的一种简单而直观的任务 ——Natural Synthetic Anomalies (NSA)，本文利用正常训练数据培训端到端模型进行异常检测和定位。NSA 技术使用 Poisson 图像编辑，无缝地混合来自不同图像的各种规模的缩放补丁，创造出一种更类似于自然子图像的不规则性的合成异常。我们使用自然和医学图像评估了所提出的方法。在 MVTec AD 数据集上的实验表明，使用本文提出的 NSA 技术训练的模型推广到检测出不同类型的制造缺陷，取得了 97.2 的总体检测 AUROC，优于所有先前的不使用额外数据集进行学习的方法。