The paper formalizes continual semi-supervised anomaly detection and presents a VAE-based baseline with generative replay and outlier rejection, reporting AUC scores on three image datasets.
One-class classification for heart disease diagnosis
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Deep evolving semi-supervised anomaly detection
The paper formalizes continual semi-supervised anomaly detection and presents a VAE-based baseline with generative replay and outlier rejection, reporting AUC scores on three image datasets.