Unsupervised Disentangled Representation Learning with Analogical Relations
This addresses the problem of enabling AI to learn interpretable data factors without supervision, though it appears incremental as it builds on existing generative models.
The paper tackles unsupervised disentangled representation learning by proposing an analogical training strategy for generative models, achieving competitive performance with state-of-the-art methods.
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. In addition, we propose a disentanglement metric called the subspace score, which is inspired by subspace learning methods and does not require supervised information. Experiments show that our proposed training strategy allows the generative models to find the disentangled factors, and that our methods can give competitive performances as compared with the state-of-the-art methods.