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What is the current state of art for anomaly detection models that can learn continuously and build classes for detected anomalies?
My use case is anomaly detection on industrial parts images.
Some parts have visual defaults, some have not. However, we don't know what default we will find on parts.
I would to explore the current state of art solutions to see what is possible, what is not possible, what need some research to be carried on.
Level 1: the basic anomaly detection model
The most basic model is to detect anomalies. It is a binary problem. Either the parts …!-->