KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Published by

Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation

  1. Devendra Singh Dhami(Technische Universität Darmstadt, University of Texas at Dallas)
  2. Mayukh Das(Microsoft Research, University of Texas at Dallas)
  3. Sriraam Natarajan(University of Texas at Dallas)


  1. Interplay between logic & neural and other learning paradigms
  2. Knowledge-driven decision making
  3. Neural-symbolic learning


While Generative Adversarial Networks (GANs) have accelerated the use of generative modelling within the machine learning community, most of the adaptations of GANs are restricted to images. The use of GANs to generate clinical data has been rare due to the inability of GANs to faithfully capture the intrinsic relationships between features given a small amount of observational data. We hypothesize and verify that this challenge can be mitigated by incorporating rich domain knowledge in the form of expert advice in the generative process. Specifically, we propose human-allied GANs that uses correlation advice from humans to create synthetic clinical data. We construct a system that takes a symbolic representation of the expert advice and converts it into constraints on correlation of the features during the generative process. Our empirical evaluation demonstrates (a) the superiority of our approach over other GAN models, (b) the importance of incorporating advice over instance noise and (c) an initial framework for incorporation of privacy in our model while capturing the relationships between features.