Leveraging sparse and shared feature activations for disentangled representation learning

Understand

Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings.

• Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data.
• In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation.
• Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate.