论文标题
Discond-Vae：将连续因素与离散删除
Discond-VAE: Disentangling Continuous Factors from the Discrete
论文作者
论文摘要
在实际数据中，每个类（例如类别标签）和每个类的独家变化都有共同的变化。我们提出了一种能够解开这两个变化的VAE变体。为了代表数据的这些生成因素，我们介绍了两组连续变量，私人变量和公共变量。我们提出的框架将私人变量分别为高斯和公共变量的混合物，分别为高斯。私有变量的每种模式都负责一类离散变量。 以前的大多数试图将离散生成因子整合到分解的尝试都假定连续变量和离散变量之间的统计独立性。我们所提出的模型（我们称之为Discond-vae）通过引入私人变量，将依赖类的连续因素与离散因素相关联。实验表明，Discond-Vae可以从数据中发现私人和公共因素。此外，即使在数据集下仅具有公共因素，Discond-vae也不会失败，并适应了私人变量以代表公共因素。
In the real-world data, there are common variations shared by all classes (e.g. category label) and exclusive variations of each class. We propose a variant of VAE capable of disentangling both of these variations. To represent these generative factors of data, we introduce two sets of continuous latent variables, private variable and public variable. Our proposed framework models the private variable as a Mixture of Gaussian and the public variable as a Gaussian, respectively. Each mode of the private variable is responsible for a class of the discrete variable. Most of the previous attempts to integrate the discrete generative factors to disentanglement assume statistical independence between the continuous and discrete variables. Our proposed model, which we call Discond-VAE, DISentangles the class-dependent CONtinuous factors from the Discrete factors by introducing the private variables. The experiments show that Discond-VAE can discover the private and public factors from data. Moreover, even under the dataset with only public factors, Discond-VAE does not fail and adapts the private variables to represent the public factors.
