Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transformations of the input data, and another that represents the transformed data. We find that the latter naturally captures the intrinsic appearance of the data. To realize the generative model, we propose a Variationally Inferred Transformational Autoencoder (VITAE) that incorporates a spatial transformer into a variational autoencoder. We show how to perform inference in the model efficiently by carefully designing the encoders and restricting the transformation class to be diffeomorphic. Empirically, our model separates the visual style from digit type on MNIST, and separates shape and pose in images of the human body.

通过一个双重潜在空间信息的生成模型，我们利用空间变换器和变分自动编码器构成了一种具有归纳偏差的Variationally Inferred Transformational Autoencoder(VITAE)方法用于实现解缠表示的学习，实验结果表明，我们的模型在MNIST图像上有效区分了数字类型和视觉风格，对于CelebA数据集，能够将人脸外形和姿态以及面部特征与面部形状分开。

生成模型中外观和视角的明确分离