Anomaly detection for OOD and novel category detection | Amazon Science

October 2021, Tom Dietterich, Emeritus Professor of Computer Science at Oregon State University, and considered one of the pioneers in the machine learning field, gave a keynote presentation at Amazon's annual machine learning conference.
Thomas discussed how every deployed learning system should be accompanied by a competence model that can detect when new queries fall outside its region of competence.
His presentation explores the application of anomaly detection to provide a competence model for object classification in deep learning. He considers two threats to competence: queries that are out-of-distribution and queries that correspond to novel classes.
Thomas reviews the four main strategies for anomaly detection and then surveys some of the many recently-published methods for anomaly detection in deep learning. The central challenge is to learn a representation that assigns distinct representations to the anomalies.
The talk concludes with a discussion of how to set the anomaly detection threshold to achieve a desired missed-alarm rate without relying on labeled anomaly data.
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