IEEE Trans Cybern. 2022 Jun;52(6):5161-5173. doi: 10.1109/TCYB.2020.3027724. Epub 2022 Jun 16.
Accurate and automated detection of anomalous samples in an image dataset can be accomplished with a probabilistic model. Such images have heterogeneous complexity, however, and a probabilistic model tends to overlook simply shaped objects with small anomalies. The reason is that a probabilistic model assigns undesirable lower likelihoods to complexly shaped objects, which are nevertheless consistent with the current set standards. This difficulty is critical, especially for a defect detection task, where the anomaly can be a small scratch or grime. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs). We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on toy datasets, two in-house manufacturing datasets, and on open manufacturing and medical datasets. The empirical results demonstrate that the unregularized score is robust to the apparent complexity of given samples and detects anomalies selectively.
可以使用概率模型准确且自动地检测图像数据集中的异常样本。然而，这些图像具有异构的复杂性，概率模型往往会忽略形状简单且仅有小异常的物体。原因是概率模型会给形状复杂的物体赋予不理想的低可能性，而这些物体与当前的标准集是一致的。这种困难对于缺陷检测任务来说尤为关键，因为异常可能是一个小划痕或污垢。为了克服这个困难，我们提出了一种用于深度生成模型（DGM）的无正则化得分。我们发现，DGM 的正则化项会根据样本的复杂性对异常得分产生重大影响。通过去除这些项，我们得到了一个无正则化的得分，我们在玩具数据集、两个内部制造数据集以及公开的制造和医疗数据集上对其进行了评估。实验结果表明，无正则化得分对给定样本的明显复杂性具有鲁棒性，并能选择性地检测异常。
