Latent space conditioning for improved classification and anomaly detection

Understand

We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label.

• Anomalies however are not known or labeled.
• We call our method conditional latent space variational autonencoder since it separates the latent space by conditioning on information within the data.
• The method fits one prior distribution to each class in the dataset, effectively expanding the prior distribution to include a Gaussian mixture model.