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

Are Disentangled Representations Helpful for Abstract Visual Reasoning?