  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.
