To analyze high-dimensional and complex data in the real world, deep
generative models, such as variational autoencoder (VAE) embed data in a
low-dimensional space (latent space) and learn a probabilistic model in the
latent space. However, they struggle to accurately reproduce the probability
distribution function (PDF) in the input space from that in the latent space.
If the embedding were isometric, this issue can be solved, because the relation
of PDFs can become tractable. To achieve isometric property, we propose Rate-
Distortion Optimization guided autoencoder inspired by orthonormal transform
coding. We show our method has the following properties: (i) the Jacobian
matrix between the input space and a Euclidean latent space forms a
constantlyscaled orthonormal system and enables isometric data embedding; (ii)
the relation of PDFs in both spaces can become tractable one such as
proportional relation. Furthermore, our method outperforms state-of-the-art
methods in unsupervised anomaly detection with four public datasets.

为了解决深度生成模型在重现概率分布函数方面的局限性，本文提出了一种基于速率失真优化的自编码器，通过实现等距性质来嵌入数据空间，取得了非常良好的无监督异常检测性能，并且在四个公共数据集上优于现有方法。