  In this paper, we investigate the problem of learning disentangled
representations. Given a pair of images sharing some attributes, we aim to
create a low-dimensional representation which is split into two parts: a shared
representation that captures the common information between the images and an
exclusive representation that contains the specific information of each image.
To address this issue, we propose a model based on mutual information
estimation without relying on image reconstruction or image generation. Mutual
information maximization is performed to capture the attributes of data in the
shared and exclusive representations while we minimize the mutual information
between the shared and exclusive representation to enforce representation
disentanglement. We show that these representations are useful to perform
downstream tasks such as image classification and image retrieval based on the
shared or exclusive component. Moreover, classification results show that our
model outperforms the state-of-the-art model based on VAE/GAN approaches in
representation disentanglement.
