Comments (1)
Thank you for seeing our library!
I'd like to confirm which do you want to reference, this library (Pixyz) or JMVAE paper (https://arxiv.org/abs/1611.01891)?
In the case of JMVAE, please also check the original implementation of this arXiv paper in the following repo.
https://github.com/masa-su/jmvae
There is a link (and BibTeX) to this paper in the readme here.
In the case of Pixyz, we've not yet published a paper of this, so we can't provide a way to reference (such as BibTeX) for now.
Also, please note that this library is not limited to JMVAE. You can implement various deep generative models with Pixyz, so I'll not add only a link to the JMVAE paper to this readme. Instead, I added this link in the notebook of JMVAE's example.
https://github.com/masa-su/pixyz/blob/master/examples/jmvae.ipynb
Thank you.
from pixyz.
Related Issues (20)
- IterativeLoss may have a bug(?)
- How to get KL divergence and reconstruction error in VAE HOT 6
- Encoder is executed 2 times in VAE HOT 2
- "invalid equation" in README.md HOT 1
- Implementation of NVAE
- feature request: MultivariateNormal
- How to get progress in pixyz batch processing
- why need 'double_after_norm' in resnet.py?
- Add .mean() and .sum() in Loss classes
- Add ELBO and NLL as Loss classes HOT 1
- Write an introduction and tutorials in English
- Add the "marginalization" option
- Add the Data distribution and the degenerate distribution HOT 1
- Add von Mises-Fisher distribution
- Change the name of evaluation method in each API HOT 1
- Implementation of the MVAE model HOT 3
- typo in README.md HOT 1
- Annealing beta in the loss function HOT 2
- Your Glow or RealNVP's implementation is forgot Split Layer, I think HOT 3
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from pixyz.