Hierarchical disentangling network for object representation learning

Abstract

An object can be described as the combination of primary visual attributes. Disentangling such underlying primitives is the long-term objective of representation learning. It is observed that categories have natural hierarchical characteristics, i.e., any two objects can share some common primitives at a particular category level while possess unique traits at another. However, previous works usually operate in a flat manner (i.e., at a particular level) to disentangle the representations of objects. Even though they may obtain the primitives to constitute objects as the categories at that level, their results are obviously not efficient and complete. In this paper, we propose a Hierarchical Disentangling Network (HDN) to exploit the rich hierarchical characteristics among categories to divide the disentangling process in a coarse-to-fine manner (i.e., level-wise), such that each level only focuses on learning the specific representations and finally the common and unique representations at all levels jointly constitute the raw object. Specifically, HDN is designed based on an encoder-decoder architecture. To simultaneously ensure the level-wise disentanglement and interpretability of the encoded representations, a novel hierarchical Generative Adversarial Network (GAN) is introduced. Quantitative and qualitative evaluations on popular object datasets validate the effectiveness of our method.