Open Category Detection with PAC Guarantees
This work addresses a critical safety and accuracy issue in machine learning applications by providing theoretical guarantees for alien detection, though it is incremental as it focuses on a specific variant with known assumptions.
The paper tackles the problem of open category detection, where alien instances from unseen classes must be identified, by developing an algorithm with PAC-style guarantees on detection rates while minimizing false alarms, and demonstrates its effectiveness on synthetic and benchmark datasets.
Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a "clean" training set that contains only the target categories of interest and an unlabeled "contaminated" training set that contains a fraction $α$ of alien examples. Under the assumption that we know an upper bound on $α$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements.