  A disentangled representation encodes information about the salient factors
of variation in the data independently. Although it is often argued that this
representational format is useful in learning to solve many real-world
down-stream tasks, there is little empirical evidence that supports this claim.
In this paper, we conduct a large-scale study that investigates whether
disentangled representations are more suitable for abstract reasoning tasks.
Using two new tasks similar to Raven's Progressive Matrices, we evaluate the
usefulness of the representations learned by 360 state-of-the-art unsupervised
disentanglement models. Based on these representations, we train 3600 abstract
reasoning models and observe that disentangled representations do in fact lead
to better down-stream performance. In particular, they enable quicker learning
using fewer samples.
