jariasf / Gmvae
Licence: mit
Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering
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Gaussian Mixture Variational Autoencoder
| Tensorflow | Pytorch |
|---|---|
 |
|
Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering in PyTorch and Tensorflow. The probabilistic model is based on the model proposed by Rui Shu, which is a modification of the M2 unsupervised model proposed by Kingma et al. for semi-supervised learning. Unlike other implementations that use marginalization for the categorical latent variable, we use the Gumbel-Softmax distribution, resulting in better time complexity because of the reduced number of gradient estimations.
Dependencies
- Tensorflow. We tested our method with the 1.13.1 tensorflow version. You can Install Tensorflow by following the instructions on its website: https://www.tensorflow.org/install/pip?lang=python2.
- Caveat: Tensorflow released the 2.0 version with different changes that will not allow to execute this implementation directly. Check the migration guide for executing this implementation in the 2.0 tensorflow version.
-
PyTorch. We tested our method with the 1.3.0 pytorch version. You can Install PyTorch by following the instructions on its website: https://pytorch.org/get-started/locally/.
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Python 3.6.8. We implemented our method with the 3.6.8 version. Additional libraries include: numpy, scipy and matplotlib.
References
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