DeepAI AI Chat
Log In Sign Up

Leveraging Relational Information for Learning Weakly Disentangled Representations

by   Andrea Valenti, et al.
University of Pisa

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.


page 3

page 6


Group-disentangled Representation Learning with Weakly-Supervised Regularization

Learning interpretable and human-controllable representations that uncov...

Interventional Robustness of Deep Latent Variable Models

The ability to learn disentangled representations that split underlying ...

Modular Representations for Weak Disentanglement

The recently introduced weakly disentangled representations proposed to ...

Boxhead: A Dataset for Learning Hierarchical Representations

Disentanglement is hypothesized to be beneficial towards a number of dow...

Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation

Recent research has shown that generative models with highly disentangle...

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

Unsupervised learning enables modeling complex images without the need f...