Explicit Disentanglement of Appearance and Perspective in Generative Models
This work addresses the challenge of interpretable representation learning for computer vision, offering a method to explicitly disentangle factors like appearance and perspective, which is incremental as it builds on existing generative models.
The paper tackles the problem of disentangling appearance and perspective in images by proposing a generative model with two latent spaces, one for spatial transformations and another for intrinsic appearance. The model, VITAE, successfully separates visual style from digit type on MNIST, shape and pose in human body images, and facial features from shape on CelebA.
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, separates shape and pose in images of human bodies and facial features from facial shape on CelebA.