We propose a new supervised learning method for Variational AutoEncoder (VAE) which has a causally disentangled representation and achieves the causally disentangled generation (CDG) simultaneously. In this paper, CDG is defined as a generative model able to decode an output precisely according to the causally disentangled representation. We found that the supervised regularization of the encoder is not enough to obtain a generative model with CDG. Consequently, we explore sufficient and necessary conditions for the decoder and the causal effect to achieve CDG. Moreover, we propose a generalized metric measuring how a model is causally disentangled generative. Numerical results with the image and tabular datasets corroborate our arguments.
翻译:我们为变异自动编码器(VAE)提出了一个新的受监督的学习方法,该方法具有因果分解的表达方式,并同时实现因果分解的一代(CDG),在本文中,CDG被定义为能够按照因果分解的表达方式对输出进行精确解码的基因模型。我们发现,受监督的编码器正规化不足以获得与CDG的基因模型。因此,我们探讨了解码器的足够和必要的条件以及实现CDG的因果关系。此外,我们提出了一个通用的衡量标准,衡量模型是如何因果分解的基因的。与图像和表格数据集的数值结果证实了我们的论点。