Search Results for author:
Found 2 papers, 1 papers with code
Deep Weakly-supervised Anomaly Detection
To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
