Search Results for author:
Found 5 papers, 3 papers with code
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.
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)
The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization.
3D Anomaly Detection and Segmentation
Depth Anomaly Detection and Segmentation
+4
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
Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors
A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds.
Ranked #6 on
3D Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
