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

Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement