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cs.LG2023
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection
Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
Semi-supervised anomaly detection~(SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer…
cs.LG2022
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection
Harsh Purohit, Takashi Endo, Masaaki Yamamoto +1
This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), vari…