Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Understand

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.