Aditya Kane

Unsupervised Out-of-Distribution Detection Using Few in-Distribution Samples

This paper tackles the out-of-distribution (OOD) detection problem for natural language classifiers. While the previous OOD detection methods require large-scale in-distribution (ID) training data, we attack this problem from the few-shot perspective in an unsupervised manner where the training reli…