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Title: Disentangling Content and Style via Unsupervised Geometry Distillation

Paper content:
It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation in a different and unpredictable way.
It is rare for one to have access to a large number of data to help separate the influences.
In this paper, we present a novel framework to learn this disentangled representation in a completely unsupervised manner.
We address this problem in a two-branch Autoencoder framework.
For the structural content branch, we project the latent factor into a soft structured point tensor and constrain it with losses derived from prior knowledge.
This encourages the branch to distill geometry information.
Another branch learns the complementary style information.
The two branches form an effective framework that can disentangle object's content-style representation without any human annotation.
We evaluate our approach on four image datasets, on which we demonstrate the superior disentanglement and visual analogy quality both in synthesized and real-world data.
We are able to generate photo-realistic images with 256x256 resolution that are clearly disentangled in content and style.
Content and style are the two most inherent attributes that characterize an object visually.
Computer vision researchers have devoted decades of efforts to understand object shape and extract features that are invariant to geometry change BID11 BID33 BID36 BID26 .
Learning such disentangled deep representation for visual objects is an important topic in deep learning.The main objective of our work is to disentangle object's style and content in an unsupervised manner.
Achieving this goal is non-trivial due to three reasons:
1) Without supervision, we can hardly guarantee the separation of different representations in the latent space.
2) Although some methods like InfoGAN are capable of learning several groups of independent attributes from objects, attributes from these unsupervised frameworks are uninterpretable since we cannot pinpoint which portion of the disentangled representation is related to the content and which to the style.
3) Learning structural content from a set of natural real-world images is difficult.To overcome the aforementioned challenges, we propose a novel two-branch Autoencoder framework, of which the structural content branch aims to discover semantically meaningful structural points (i.e., y in Fig
2) to represent the object geometry, while the other style branch learns the complementary style representation.
The settings of these two branches are asymmetric.
For the structural content branch, we add a layer-wise softmax operator to the last layer.
We could regard this as a projection of a latent content to a soft structured point tensor space.
Specifically designed prior losses are used to constrain the structured point tensors so that the discovered points have high repeatability across images yet distributed uniformly to cover different parts of the object.
To encourage the framework to learn a disentangled yet complementary representation of both content and style, we further introduce a Kullback-Leibler (KL) divergence loss and skip-connections design to the framework.
In FIG0 , we show the latent space walking results on cat face dataset, which demonstrates a reasonable coverage of the manifold and an effective disentanglement of the content and style space of our approach.Extensive experiments show the effectiveness of the proposed method in disentangling the content and style of natural images.
We also conduct qualitative and quantitative experiments on MNISTColor, 3D synthesized data and several real-world datasets which demonstrate the superior performance of our method to state-of-the-art algorithms.
We propose a novel model based on Autoencoder framework to disentangle object's representation by content and style.
Our framework is able to mine structural content from a kind of objects and learn content-invariant style representation simultaneously, without any annotation.
Our work may also reveal several potential topics for future research:
1) Instead of relying on supervision, using strong prior to restrict the latent variables seems to be a potential and effective tool for disentangling.
2) In this work we only experiment on near-rigid objects like chairs and faces, learning on deformable objects is still an opening problem.3) The content-invariant style representation may have some potentials on recognition tasks.

assistant: text: We present a novel framework to learn the disentangled representation of content and style in a completely unsupervised manner.