Leveraging Relational Information for Learning Weakly Disentangled Representations
This work addresses the problem of restrictive disentanglement definitions for machine learning practitioners, offering an incremental improvement by focusing on relational information.
The paper tackles the difficulty of enforcing disentanglement in neural representations by proposing a weakly disentangled approach that leverages relational learning to identify and relate latent regions for controlled changes, showing that it separates factors of variation while preserving generation quality.
Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation. We argue that such a definition might be too restrictive and not necessarily beneficial in terms of downstream tasks. In this work, we present an alternative view over learning (weakly) disentangled representations, which leverages concepts from relational learning. We identify the regions of the latent space that correspond to specific instances of generative factors, and we learn the relationships among these regions in order to perform controlled changes to the latent codes. We also introduce a compound generative model that implements such a weak disentanglement approach. Our experiments shows that the learned representations can separate the relevant factors of variation in the data, while preserving the information needed for effectively generating high quality data samples.