  Novelty detection using deep generative models such as autoencoder,
generative adversarial networks mostly takes image reconstruction error as
novelty score function. However, image data, high dimensional as it is,
contains a lot of different features other than class information which makes
models hard to detect novelty data. The problem gets harder in multi-modal
normality case. To address this challenge, we propose a new way of measuring
novelty score in multi-modal normality cases using orthogonalized latent space.
Specifically, we employ orthogonal low-rank embedding in the latent space to
disentangle the features in the latent space using mutual class information.
With the orthogonalized latent space, novelty score is defined by the change of
each latent vector. Proposed algorithm was compared to state-of-the-art novelty
detection algorithms using GAN such as RaPP and OCGAN, and experimental results
show that ours outperforms those algorithms.
