Chongjun Wang

Never Ignore the Significance of Different Anomalies: A Cost-Sensitive Algorithm Based on Loss Function for Anomaly Detection

2015

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried …

CESED: Exploiting Hyperspherical Predefined Evenly-Distributed Class Centroids for OOD Detection

2023 · Society for Industrial and Applied Mathematics eBooks

Out-of-distribution (OOD) detection is critical for ensuring the safe deployment of machine learning models in the open world. Due to the simplicity and intuitiveness of distance- based methods, i.e., samples are detected as OOD if …

READ: Aggregating Reconstruction Error into Out-of-Distribution Detection

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abnormal data. Existing works directly design score function …