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jumpynitro / MPCC

MPCC: Matching Priors and Conditional for Clustering. Official implementation
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adversarial clustering gans generative vae

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Generative adversarial network for clustering.

MPCC: Matching Priors and Conditionals for Clustering

Official implemenation of MPCC: Matching Priors and conditionals for Clustering. This respository is strongly based on ''The author's officially unofficial PyTorch BigGAN'' implementation.

How To Use This Code

You will need:

Note that the official FID score and IS are based on tensorflow implementations. You will need tensorflow 1.1 and 1.3 respectively to obtain these official metrics using inception_tf13_p.py and fid_p.py. You can find C10 inception metrics in here.

A jupyter notebook is provided to perform generation, reconstructions and predictions with MPCC. Additionally Cifar10 models weights are included.

Here are some samples of the generative model:

Cifar10 samples (every two columns a different cluster):

Cifar10 samples

Cifar20 samples (every row a different cluster):

Cifar20 samples

Omniglot samples (every row a different cluster):

Omniglot samples