Muhammad Aqeel

Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning

So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manual data curation, introducing bias and limiting adaptability. We propose Confident Meta-learning (CoMet), a novel trainin…