论文标题
通过可靠的变分自动编码来检测新颖性
Novelty Detection via Robust Variational Autoencoding
论文作者
论文摘要
我们提出了一种新的新方法来探测，可以容忍训练点的高腐败，而以前的作品则假定没有或非常低的腐败。我们的方法训练了强大的变异自动编码器（VAE），该自动编码器（VAE）旨在为未腐败的训练点生成模型。为了获得对高腐败的鲁棒性，我们通过以下四个更改进行了共同VAE的四个更改：1。通过精心设计的缩小尺寸的分布组件提取潜在代码的关键特征； 2。将潜在分布建模为高斯低级别嵌入式和全等级异常值的混合物，其中测试仅使用嵌入式模型； 3。应用Wasserstein-1度量标准进行正则化，而不是Kullback-Leibler（KL）差异； 4。使用可靠的错误进行重建。我们既对异常值建立鲁棒性，又建立了对Wasserstein指标低排名建模而不是KL Divergence的适用性。我们说明了标准基准的最新结果。
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.
