Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this study proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO. Code is available on https://github.com/caoyunkang/CDO.

翻译：大多数无监督图像异常定位方法因卷积神经网络的高泛化能力而存在过度泛化问题，导致预测不可靠。为缓解过度泛化，本研究提出利用合成异常协同优化正常与异常特征分布，即协同差异优化（CDO）。CDO引入边缘优化模块与重叠优化模块，以优化决定定位性能的两个关键因素——正常样本与异常样本差异分布之间的边缘与重叠度。通过CDO，可获得正常与异常差异分布的大边缘与小重叠，从而提升预测可靠性。在MVTec2D与MVTec3D数据集上的实验表明，CDO能有效缓解过度泛化，并以实时计算效率实现优异的异常定位性能。汽车塑料零部件实际检测应用进一步验证了所提CDO的能力。代码开源于https://github.com/caoyunkang/CDO。