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Disentangling Factors of Variation with Cycle-Consistent Variational Auto-encoders

  • Ananya Harsh JhaEmail author
  • Saket Anand
  • Maneesh Singh
  • VSR Veeravasarapu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)

Abstract

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.

Keywords

Disentangling factors of variation Cycle-consistent architecture Variational auto-encoders 

Notes

Acknowledgement

We are thankful for the insightful feedback from anonymous ECCV reviewers. We acknowledge Infosys Center for AI at IIIT-Delhi for partially supporting this research. We also appreciate the support from Verisk Analytics for its successful execution.

Supplementary material

474178_1_En_49_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (pdf 1214 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ananya Harsh Jha
    • 1
    Email author
  • Saket Anand
    • 1
  • Maneesh Singh
    • 2
  • VSR Veeravasarapu
    • 2
  1. 1.IIIT-DelhiDelhiIndia
  2. 2.Verisk AnalyticsJersey CityUSA

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