Search Results for author:
Found 5 papers, 4 papers with code
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies, and 2) effectively generalize to unseen anomalies in new domains.
Background Matters: Enhancing Out-of-distribution Detection with Domain Features
To this end, we propose a novel generic framework that can learn the domain features from the ID training samples by a dense prediction approach, with which different existing semantic-feature-based OOD detection methods can be seamlessly combined to jointly learn the in-distribution features from both the semantic and domain dimensions.
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes.
Ranked #1 on
Anomaly Detection
on Fishyscapes
(using extra training data)
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screening, etc.
Ranked #4 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
Explainable Deep Few-shot Anomaly Detection with Deviation Networks
Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models.
Ranked #5 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
