Title: CIGMO: Learning categorical invariant deep generative models from grouped data. Abstract: Images of general objects are often composed of three hidden factors: category (e.g., car or chair), shape (e.g., particular car form), and view (e.g., 3D orientation).   While there have been many disentangling models that can discover either a category or shape factor separately from a view factor, such models typically cannot capture the structure of general objects that the diversity of shapes is much larger across categories than within a category.   Here, we propose a novel generative model called CIGMO, which can learn to represent the category, shape, and view factors at once only with weak supervision.  Concretely, we develop mixture of disentangling deep generative models, where the mixture components correspond to object categories and each component model represents shape and view in a category-specific and mutually invariant manner.  We devise a learning method based on variational autoencoders that does not explicitly use label information but uses only grouping information that links together different views of the same object.  Using several datasets of 3D objects including ShapeNet, we demonstrate that our model often outperforms previous relevant models including state-of-the-art methods in invariant clustering and one-shot classification tasks, in a manner exposing the importance of categorical invariant representation.
