OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space

• URL: http://arxiv.org/abs/2101.02358v1
• Date: Thu, 7 Jan 2021 03:59:47 GMT
• Title: OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space
• Authors: Sungkwon An, Jeonghoon Kim, Myungjoo Kang, Shahbaz Razaei and Xin Liu
• Abstract summary: We propose a new way of measuring novelty score in multi-modal normality cases using orthogonalized latent space. 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.
• Score: 2.8460045437436383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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