Latent space conditioning for improved classification and anomaly detection
We propose a variational autoencoder to perform improved pre-processing forclustering and anomaly detection on data with a given label. Anomalies howeverare not known or labeled. We call our method conditioned variationalautonencoder since it separates the latent space by conditioning on informationwithin the data. The method fits one prior distribution to each class in thedataset, effectively exp
