Implementation of Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection.
TensorFlow implementation of Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. [PyTorch Version] [TensorFlow 2 Version]
Architecture of MemAE.
Graph of MemAE.
‘Class-1’ is defined as normal and the others are defined as abnormal.
Restoration result by MemAE.
Box plot and histogram of restoration loss in test procedure.
[1] Dong Gong et al. (2019). Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. arXiv preprint arXiv:1904.02639.