Large intra-class variation is the result of changes in multiple object
characteristics. Images, however, only show the superposition of different
variable factors such as appearance or shape. Therefore, learning to
disentangle and represent these different characteristics poses a great
challenge, especially in the unsupervised case. Moreover, large object
articulation calls for a flexible part-based model. We present an unsupervised
approach for disentangling appearance and shape by learning parts consistently
over all instances of a category. Our model for learning an object
representation is trained by simultaneously exploiting invariance and
equivariance constraints between synthetically transformed images. Since no
part annotation or prior information on an object class is required, the
approach is applicable to arbitrary classes. We evaluate our approach on a wide
range of object categories and diverse tasks including pose prediction,
disentangled image synthesis, and video-to-video translation. The approach
outperforms the state-of-the-art on unsupervised keypoint prediction and
compares favorably even against supervised approaches on the task of shape and
appearance transfer.

提出了一种基于无监督学习的方法，通过学习物体的不同特征，如表现和形状，来实现特征的分离和表示，并使用等变约束进行训练，适用于任意类别，并在各种任务中表现优异。