mmd-variational-autoencoder's People
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zeitgeistqian tpnguyen batterysnoopy lethienhoa kylelamarrc pratikm141 yaolezju jaykimbravekjh kiwi0fruit hoangcuong2011 lbnphoenix aijinz bgtoo domkirkham pren1 daydreamer2023 coolsunxu wangxinqi94 fgitmichael maggielieu walwi878 sean0719 eribertoo 183amir kingofasianpopjc fshimaa liubruce mustphd ed-ortizm jgonagle fcaretti xiongdaowenmmd-variational-autoencoder's Issues
Shouldn't z be a Gaussian distribution? seems that in code your code you just take out the output of a hidden layer (train_z) and then you minimize the MMD of that with a prior!
question about disengtanglement
On your last statement in your blog post - http://szhao.me/2017/06/10/a-tutorial-on-mmd-variational-autoencoders.html you wrote "We can observe good disentangling." I wanna know how is disentanglement visualized there?
Implementation in Keras
Hello, thank you for your code! It's quite useful. I just reimplement your code in Keras, at here: https://github.com/pren1/keras-MMD-Variational-Autoencoder.git
I hope someone will find this link useful.
The implementation is not variational
Hi Shengija,
Thank you for your work.
The implementation does not seem to be a variational approach. The mapping between the x and z is deterministic with no Gaussian density estimation and sampling.
I may be misinterpretting here, but I can't match this to the InfoVAE paper: https://arxiv.org/pdf/1706.02262.pdf
Thanks,
Amir
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