We propose a new method for novelty detection that can tolerate high corruption of the training points, whereas previous works assumed either no or very low corruption. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points. To gain robustness to high corruption, we incorporate the following four changes to the common VAE: 1. Extracting crucial features of the latent code by a carefully designed dimension reduction component for distributions; 2. Modeling the latent distribution as a mixture of Gaussian low-rank inliers and full-rank outliers, where the testing only uses the inlier model; 3. Applying the Wasserstein-1 metric for regularization, instead of the Kullback-Leibler (KL) divergence; and 4. Using a robust error for reconstruction. We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence. We illustrate state-of-the-art results on standard benchmarks.

翻译：我们提出了一种新的新发现方法,可以容忍训练点高度腐败,而以前的工作假设不是没有腐败,就是非常低腐败。我们的方法训练了强大的变式自动coder(VAE),目的是为不受干扰的培训点制作一个模型。为了对高度腐败形成强力,我们将以下四个修改纳入共同的VAE:1. 通过仔细设计的尺寸减少部分来提取潜在代码的关键特征,用于分发;2. 模拟潜在分布,作为高斯低级内线和全级外线的混合体,而测试只使用离子模型;3. 应用瓦塞斯坦-1衡量标准规范化,而不是Kullback-Leiber(KL)差异;和4. 利用强性错误重建。我们既要建立对外线的坚固性,又要适合瓦塞斯坦指标的低级建模,而不是KL差异。我们要说明标准基准方面的最新成果。</s>