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The problem of detecting a novel class at run time is known as Open SetDetection & is important for various real-world applications like medicalapplication, autonomous driving, etc. Open Set Detection within context of deeplearning involves solving two problems: (i) Must map the input images into alatent representation that contains enough information to detect the outliers,and (ii) Must learn an anomaly scoring function that can extract thisinformation from the latent representation to identify the anomalies. Researchin deep anomaly detection methods has progressed slowly. One reason may be thatmost papers simultaneously introduce new representation learning techniques andnew anomaly scoring approaches. The goal of this work is to improve thismethodology by providing ways of separately measuring the effectiveness of therepresentation learning and anomaly scoring. This work makes two methodologicalcontributions. The first is to introduce the notion of Oracle anomaly detectionfor quantifying the information available in a learned latent representation.The second is to introduce Oracle representation learning, which produces arepresentation that is guaranteed to be sufficient for accurate anomalydetection. These two techniques help researchers to separate the quality of thelearned representation from the performance of the anomaly scoring mechanism sothat they can debug and improve their systems. The methods also provide anupper limit on how much open category detection can be improved through betteranomaly scoring mechanisms. The combination of the two oracles gives an upperlimit on the performance that any open category detection method could achieve.This work introduces these two oracle techniques and demonstrates their utilityby applying them to several leading open category detection methods.

Oracle Analysis of Representations for Deep Open Set Detection