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PyTorch implementation of Generative Adversarial Networks (GAN)

Implemented models:

Analyzed Datasets:

MNIST

GAN

Learning curves

gan_metrics

Generated examples

examples.mp4

DCGAN

Learning curves

dcgan_metrics

Generated examples

video.mp4

Conditional DCGAN

Learning curves

conditional_dcgan_metrics

Examples (each row is conditioned with specific digit)

examples.mp4

CelebA

GAN

Learning curves

gan_metrics

Generated examples

video.mp4

DCGAN

Learning curves

gan_metrics

Generated examples

video.mp4

Interpolation of latent variable

For each latent dimension latent_dim a new z noise was sampled from gaussian distribution and z[latent_dim] was then interpolated between -3.5 and 3.5

CelebA_dcgan

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GAN implementation in PyTorch

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Readme

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MIT license
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