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Problems of IWAE ELBO Loss
See original GitHub issueHi Anand and all,
As weighting of samples, weight should be detached from the current computational graph for the expected optimization objective, right? See
https://github.com/AntixK/PyTorch-VAE/blob/8700d245a9735640dda458db4cf40708caf2e77f/models/iwae.py#L155
Issue Analytics
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- Created 2 years ago
- Reactions:1
- Comments:5
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Besides, as the original paper said, “Vanilla VAE separated out the KL divergence in the bound in order to achieve a simpler and lower-variance update. Unfortunately, no analogous trick applies for k > 1” (Y. Burda et al., 2016). How are we still able to compute KL Divergence? https://github.com/AntixK/PyTorch-VAE/blob/8700d245a9735640dda458db4cf40708caf2e77f/models/iwae.py#L152
Kindly refers to PR: https://github.com/AntixK/PyTorch-VAE/pull/53