@@ -13,6 +13,7 @@ Collection of PyTorch implementations of Generative Adversarial Network varietie
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+ [ BEGAN] ( #began )
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+ [ BicycleGAN] ( #bicyclegan )
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+ [ Boundary-Seeking GAN] ( #boundary-seeking-gan )
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+ + [ Cluster GAN] ( #cluster-gan )
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+ [ Conditional GAN] ( #conditional-gan )
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+ [ Context-Conditional GAN] ( #context-conditional-gan )
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+ [ Context Encoder] ( #context-encoder )
@@ -149,6 +150,39 @@ $ cd implementations/bgan/
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$ python3 bgan.py
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```
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+ ### Cluster GAN
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+ _ ClusterGAN: Latent Space Clustering in Generative Adversarial Networks_
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+
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+ #### Authors
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+ Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan
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+
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+ #### Abstract
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+ Generative Adversarial networks (GANs) have obtained remarkable success in many unsupervised learning tasks and
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+ unarguably, clustering is an important unsupervised learning problem. While one can potentially exploit the
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+ latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the
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+ GAN latent space. In this paper, we propose ClusterGAN as a new mechanism for clustering using GANs. By sampling
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+ latent variables from a mixture of one-hot encoded variables and continuous latent variables, coupled with an
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+ inverse network (which projects the data to the latent space) trained jointly with a clustering specific loss, we
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+ are able to achieve clustering in the latent space. Our results show a remarkable phenomenon that GANs can preserve
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+ latent space interpolation across categories, even though the discriminator is never exposed to such vectors. We
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+ compare our results with various clustering baselines and demonstrate superior performance on both synthetic and
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+ real datasets.
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+
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+ [[ Paper]] ( https://arxiv.org/abs/1809.03627 ) [[ Code]] ( implementations/cluster_gan/clustergan.py )
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+
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+ Code based on a full PyTorch [[ implementation]] ( https://github.com/zhampel/clusterGAN ) .
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+
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+ #### Run Example
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+ ```
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+ $ cd implementations/cluster_gan/
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+ $ python3 clustergan.py
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+ ```
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+
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+ <p align =" center " >
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+ <img src="assets/cluster_gan.gif" width="360"\>
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+ </p >
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+
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+
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### Conditional GAN
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_ Conditional Generative Adversarial Nets_
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