Domain-independent detection of known anomalies
This addresses industrial quality inspection by enabling anomaly detection across new objects, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of detecting known anomalies in unseen domains with sparse data, introducing a hybrid task of domain generalization on sparse classes and achieving an average image-level AUROC of 87.2% with their Spatial Embedding MLP method.
One persistent obstacle in industrial quality inspection is the detection of anomalies. In real-world use cases, two problems must be addressed: anomalous data is sparse and the same types of anomalies need to be detected on previously unseen objects. Current anomaly detection approaches can be trained with sparse nominal data, whereas domain generalization approaches enable detecting objects in previously unseen domains. Utilizing those two observations, we introduce the hybrid task of domain generalization on sparse classes. To introduce an accompanying dataset for this task, we present a modification of the well-established MVTec AD dataset by generating three new datasets. In addition to applying existing methods for benchmark, we design two embedding-based approaches, Spatial Embedding MLP (SEMLP) and Labeled PatchCore. Overall, SEMLP achieves the best performance with an average image-level AUROC of 87.2 % vs. 80.4 % by MIRO. The new and openly available datasets allow for further research to improve industrial anomaly detection.