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Found 4 papers, 2 papers with code
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization
The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures.
Ranked #13 on
Anomaly Detection
on VisA
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.
Ranked #11 on
Anomaly Detection
on VisA
(Detection AUROC metric)
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.
Measuring the Accuracy of Object Detectors and Trackers
However, evaluating the accuracy of object detectors and trackers that are restricted to boxes on densely segmented data is not straightforward.
