  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.
