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Anomalous Example Detection in Deep Learning: A Survey
[article]
2021
arXiv
pre-print
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions
arXiv:2003.06979v2
fatcat:4mogo75b4rbxrc6vph2xmllkue