manufacturingunsupervised anomaly detection benchmarking via normality modeling, student-teacher discrepancy, reconstruction, diffusion, and synthetic anomaly training
Benchmarking unsupervised anomaly detection under realistic factory inspection conditions
Compare different AI methods that learn what normal products look like, then flag anything unusual, using a test set that looks like the real factory where almost everything is good and only a few items are bad.