论文标题
使用加权共轭功能二重性，强大的生成限制内核机器
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
论文作者
论文摘要
在过去的十年中，人们对生成模型的兴趣已经巨大。但是，他们的训练性能可能会受到污染的不利影响，在该污染物中，在模型的表示中进行了编码。这导致产生嘈杂的数据。在本文中，我们在限制内核计算机（RKMS）的框架中介绍了加权共轭特征二元性。 RKM公式允许从经典鲁棒统计数据中轻松整合方法。该公式用于使用基于最小协方差的决定因素的加权函数来微调生成rkms的潜在空间，这是多变量位置和散布的高度健壮的估计器。实验表明，加权RKM能够在训练数据中存在污染时产生干净的图像。我们进一步表明，强大的方法还通过标准数据集上的定性和定量实验来保留不相关的特征学习。
Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded in the representation of the model. This results in the generation of noisy data. In this paper, we introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). The RKM formulation allows for an easy integration of methods from classical robust statistics. This formulation is used to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a highly robust estimator of multivariate location and scatter. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data. We further show that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets.
