Memory Augmented Variational Auto-Encoder for Anomaly Detection

Anomaly detection is of great importance due to its wide application in industrial areas. Deep auto-encoders have enabled significant advances in anomaly detection over the past decade. However, it has been observed that sometimes the auto-encoder are likely to reconstruct anomalies due to the “excellent” generalization ability, resulting in the miss detection of anomalies. To mitigate this deficiency, scholars have proposed the memory-augmented deep auto-encoder method recently. However, more recent work on memory-augmented uses continuous memory representations or key-values pairs in memory for read/write without forgetting mechanism, and it is assumed that data such as early access to the network are normal data. To deal with those imperfectness, we present a VAE-based memory-augmented network with a trained memory network: MEMVAE, addressing scheme to provide a strong guarantee to detect anomalous. In particular, Gated Recurrent Unit (GRU) cells are employed as the encoder and decoder to capture latent temporal structure. We then train an external memory module that records common patterns of the prototype. In addition, we present a weighted regular score that simply updates our memory entries. Our detector reports a reconstruction probability as the anomaly score. Experimental results on tabular data and time series data show that MEMVAE demonstrates significant improvements and better performance.