  Recent work by Locatello et al. (2018) has shown that an inductive bias is
required to disentangle factors of interest in Variational Autoencoder (VAE).
Motivated by a real-world problem, we propose a setting where such bias is
introduced by providing pairwise ordinal comparisons between instances, based
on the desired factor to be disentangled. For example, a doctor compares pairs
of patients based on the level of severity of their illnesses, and the desired
factor is a quantitive level of the disease severity. In a real-world
application, the pairwise comparisons are usually noisy. Our method, Robust
Ordinal VAE (ROVAE), incorporates the noisy pairwise ordinal comparisons in the
disentanglement task. We introduce non-negative random variables in ROVAE, such
that it can automatically determine whether each pairwise ordinal comparison is
trustworthy and ignore the noisy comparisons. Experimental results demonstrate
that ROVAE outperforms existing methods and is more robust to noisy pairwise
comparisons in both benchmark datasets and a real-world application.
