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A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-learning

  • Michael KissnerEmail author
  • Helmut Mayer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)

Abstract

We follow the idea of formulating vision as inverse graphics and propose a new type of element for this task, a neural-symbolic capsule. It is capable of de-rendering a scene into semantic information feed-forward, as well as rendering it feed-backward. An initial set of capsules for graphical primitives is obtained from a generative grammar and connected into a full capsule network. Lifelong meta-learning continuously improves this network’s detection capabilities by adding capsules for new and more complex objects it detects in a scene using few-shot learning. Preliminary results demonstrate the potential of our novel approach.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Applied Computer ScienceBundeswehr University MunichNeubibergGermany

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