Reinterpreting Importance-Weighted AutoencodersDownload PDF

Oct 17, 2021 (edited Feb 27, 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:,,
3 Replies