Data of general object images have two most common structures: (1) each
object of a given shape can be rendered in multiple different views, and (2)
shapes of objects can be categorized in such a way that the diversity of shapes
is much larger across categories than within a category. Existing deep
generative models can typically capture either structure, but not both. In this
work, we introduce a novel deep generative model, called CIGMO, that can learn
to represent category, shape, and view factors from image data. The model is
comprised of multiple modules of shape representations that are each
specialized to a particular category and disentangled from view representation,
and can be learned using a group-based weakly supervised learning method. By
empirical investigation, we show that our model can effectively discover
categories of object shapes despite large view variation and quantitatively
supersede various previous methods including the state-of-the-art invariant
clustering algorithm. Further, we show that our approach using
category-specialization can enhance the learned shape representation to better
perform down-stream tasks such as one-shot object identification as well as
shape-view disentanglement.

本文提出了一种名为 CIGMO 的新型深度生成模型，可以从图像数据中学习表示类别、形状和视图等因素。通过实验证明，该模型可以有效地发现物体形状的类别，即使视角差异很大，并且比以前的方法更好地完成了诸如单次物体识别和形状 - 视图解缠的任务。