DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping
- Resource Type
- Conference
- Authors
- Schau, Geoffrey; Burlingame, Erik; Chang, Young Hwan
- Source
- 2020 59th IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2020 59th IEEE Conference on. :5092-5097 Dec, 2020
- Subject
- Robotics and Control Systems
Biomedical measurement
Deep learning
Toy manufacturing industry
Task analysis
Sequential analysis
Mutual information
Mathematical model
- Language
- ISSN
- 2576-2370
Deep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domain- specific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non- sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.