42 citations · 44 across the 2 of their papers we have counts for
2 papers
stat.ML2020★ 42 cited
Why is the Mahalanobis Distance Effective for Anomaly Detection?
Ryo Kamoi, Kei Kobayashi
The Mahalanobis distance-based confidence score, a recently proposed anomaly detection method for pre-trained neural classifiers, achieves state-of-the-art performance on both out-…
stat.ML2019★ 2 cited
Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice
Ryo Kamoi, Kei Kobayashi
Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is…