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.

本文提出一种通过正交化低秩嵌入来度量多模态正态性中新奇性得分的新方法，通过该方法与使用 GAN 的 RaPP 和 OCGAN 等现有新颖性检测算法的比较，实验结果表明该方法优于那些算法。

OAAE：通过正交潜空间进行多模态正常情况下的新颖性检测的对抗自编码器