This study focused on the problem of anomaly detection (AD) by means of mixture-of-experts network. Most of the existing AD methods solely based on the reconstruction errors or latent representation using a single low-dimensional manifold are often not ideal for the image objects with complex background. However, modeling the data as a mixture of low-dimensional nonlinear manifolds is natural and promising for the classification of anomalies. In this study to realize the promise of multi-manifold latent information for AD, we propose a mixture of experts ensemble with two convolutional variational autoencoders (CVAEs) and convolution network (MEx-CVAEC) which explicitly learns manifold relationships of data that make use of multiple encoded detections. Additionally, we integrate a linear-based CAE as a gating network which optimizes the expert structures for efficient data characterization based on the manifold of the latent space. In the expert structure the data is re-encoded after each decoder to enhance the latent detection performance and the VAE is used as a core element in the encoder-decoder-encode (EDE) pipeline. To the best of our knowledge, this is the first study suggesting a mixture of CVAEs-based models for AD. The performance of the MEx-CVAE with EDE pipeline which we names as (MEx-CVAEC) compared over two basic MEx-CVAE model with ED pipeline based on logistic regression (MEx-L) and based on CAE (MEx-C) structures. In addition, the performance of the proposed model on three different datasets show the highest average AUC value than that of the state-of-the-art for image anomalies detection task.

这项研究集中在通过专家混合网络进行异常检测（AD）的问题上。大多数现有的仅基于重构误差或使用单个低维流形的潜在表示的AD方法对于具有复杂背景的图像对象通常并不理想。但是，将数据建模为低维非线性流形的混合物是很自然的，并有望用于异常分类。在这项研究中，为了实现针对AD的多歧管潜​​在信息的承诺，我们提出了专家集成与两种卷积变分自编码器（CVAE）和卷积网络（MEx-CVAEC）的混合物，后者明确学习了利用多个编码的检测。另外，我们将基于线性的CAE集成为门控网络，该网络优化了基于潜在空间流形的专家结构，可有效地进行数据表征。在专家结构中，数据在每个解码器之后进行重新编码以增强潜在检测性能，而VAE用作编码器-解码器-编码（EDE）管道中的核心元素。据我们所知，这是第一项研究，提出了基于CVAE的AD模型的混合体。在基于逻辑回归（MEx-L）和基于CAE（MEx-C）结构的两种具有ED管道的基本MEx-CVAE模型上，我们将EED管道的MEx-CVAE（MEx-CVAEC）的性能进行了比较。此外，