Reinterpreting Importance-Weighted Autoencoders
Abstract
The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. 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, present a tighter lower bound, and visualize the implicit importance-weighted distribution.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2017
- DOI:
- 10.48550/arXiv.1704.02916
- arXiv:
- arXiv:1704.02916
- Bibcode:
- 2017arXiv170402916C
- Keywords:
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- Statistics - Machine Learning
- E-Print:
- ICLR 2017 Workshop