20 citations · 44 across the 6 of their papers we have counts for
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stat.ML2019★ 8 cited
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators
Tomoharu Iwata, Yuki Yamanaka
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been w…
stat.ML2019★ 4 cited
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Yuki Yamanaka, Tomoharu Iwata, Hiroshi Takahashi +2
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervi…