Lang Yi Ang

CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias

The detection of anomalous samples in large, high-dimensional datasets is a challenging task with numerous practical applications. Recently, state-of-the-art performance is achieved with deep learning methods: for example, using the reconstruction error from an autoencoder as anomaly scores. However…