An Information Criterion for Controlled Disentanglement of Multimodal Data
Chenyu Wang Sharut Gupta Xinyi Zhang Sana Tonekaboni Stefanie Jegelka Tommi Jaakkola Caroline Uhler
Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that is shared across modalities, we can improve interpretability and robustness and enable downstream tasks such as the generation of counterfactual outcomes. Separating the two types of information is challenging since they are often deeply entangled in many real-world applications. We propose $\textbf{Disentangled}$ $\textbf{S}$elf-$\textbf{S}$upervised $\textbf{L}$earning (DisentangledSSL), a novel self-supervised approach for learning disentangled representations. We present a comprehensive analysis of the optimality of each disentangled representation, particularly focusing on the scenario not covered in prior work where the so-called $\textit{Minimum Necessary Information}$ (MNI) point is not attainable. We demonstrate that \algo successfully learns shared and modality-specific features on multiple synthetic and real-world datasets and consistently outperforms baselines on various downstream tasks, including prediction tasks for vision-language data, as well as molecule-phenotype retrieval tasks for biological data.
Multimodal Representation Learning, Disentanglement, Self-Supervised Learning, Information Theory
We propose DisentangledSSL to separate modality-specific information from shared information in multimodal data, providing theoretical guarantees and strong empirical performance, especially when Minimum Necessary Information is unattainable.