Learning disentangled representations

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by few explanatory factors of variation (e.g. content + position of objects in an image) which can be recovered by unsupervised learning algorithms. A recent line of work argued that learning such representations offer several benefits, ranging from reduced sample complexity of downstream tasks to interpretability.

In this talk, I will discuss the recent progress in the field and challenge some common assumptions. I will address the role of inductive biases in the theoretical impossibility of the unsupervised learning of disentangled representations and provide a sober look at the performances of state-of-the-art approaches. I will further present how to go beyond the purely unsupervised setting in both theory and practice and discuss concrete applications to fairness and abstract visual reasoning.