Reinterpreting Importance-Weighted AutoencodersDownload PDF

02 Dec 2020 (modified: 27 Feb 2017)ICLR 2017 workshop submissionReaders: Everyone
  • TL;DR: IWAE optimizes the standard variational lowerbound, but using a more complex variational distribution
  • Abstract: The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribution. We formally derive this result, and visualize the implicit importance-weighted approximate posterior.
  • Keywords: Unsupervised Learning
  • Conflicts:,,
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