Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Understand

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.

• In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions.
• We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data.
• Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets.