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Learning Interpretable Disentangled Representations Using Adversarial VAEs

  • Mhd Hasan SarhanEmail author
  • Abouzar Eslami
  • Nassir Navab
  • Shadi Albarqouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of \(81.50\%\) in terms of disentanglement, \(11.60\%\) in clustering, and \(2\%\) in supervised classification with a few amount of labeled data.

Keywords

Deep learning Disentangled representation Interpretability 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Aided Medical ProceduresTechnical University of MunichMunichGermany
  2. 2.Carl Zeiss Meditec AGMunichGermany
  3. 3.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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