Unsupervised Disentangled Representation Learning with Analogical Relations

Understand

Learning the disentangled representation of interpretable generative factors of data is one of the foundations to allow artificial intelligence to think like people.

• In this paper, we propose the analogical training strategy for the unsupervised disentangled representation learning in generative models.
• The analogy is one of the typical cognitive processes, and our proposed strategy is based on the observation that sample pairs in which one is different from the other in one specific generative factor show the same analogical relation.
• Thus, the generator is trained to generate sample pairs from which a designed classifier can identify the underlying analogical relation.