  Recently, researches related to unsupervised disentanglement learning with
deep generative models have gained substantial popularity. However, without
introducing supervision, there is no guarantee that the factors of interest can
be successfully recovered. Motivated by a real-world problem, we propose a
setting where the user introduces weak supervision by providing similarities
between instances based on a factor to be disentangled. The similarity is
provided as either a binary (yes/no) or a real-valued label describing whether
a pair of instances are similar or not. We propose a new method for weakly
supervised disentanglement of latent variables within the framework of
Variational Autoencoder. Experimental results demonstrate that utilizing weak
supervision improves the performance of the disentanglement method
substantially.
