Latent Distribution of Variational Autoencoder for Out-of-Distribution Detection

DOI

Bibliographic Information

Other Title
  • 変分自己符号化器による分布外検知のための潜在変数分布

Description

<p>Deep generative models, which can obtain the likelihood of each data point, are often used for out-of-distribution detection. However, deep generative models may assign a higher likelihood to out-of-distribution data. VAE is one of the deep generative models suffering from this problem. The typical prior distribution is the standard normal distribution in VAEs. We hypothesize that the latent variables of out-of-distribution data are concentrated near the origin. To solve this problem, this paper proposes an alternative prior distribution of the latent variables in VAEs. The proposed prior distribution has a low probability density near the origin. Experiments on Fashion-MNIST and MNIST show that the proposed method improves the performance of out-of-distribution detection compared to existing methods.</p>

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Keywords

Details 詳細情報について

  • CRID
    1390851320456360832
  • NII Article ID
    130008051580
  • DOI
    10.11517/pjsai.jsai2021.0_2g4gs2f05
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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