GANs
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
GPU or CPU
Support both GPU and CPU.
Dependencies
Table of Contents
Experiment Results
Vanilla GAN (GAN)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 100 |
epoch 150 |
epoch 199 |
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Conditional GAN (cGAN)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 100 |
epoch 150 |
epoch 199 |
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Improved Conditional GAN (Improved cGAN)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 100 |
epoch 150 |
epoch 199 |
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Deep Convolutional GAN (DCGAN)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 60 |
epoch 70 |
epoch 80 |
epoch 90 |
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Wasserstein GAN (WGAN)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 100 |
epoch 150 |
epoch 199 |
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Wasserstein GAN with Gradient Plenty (WGAN-GP)
epoch 0 |
epoch 10 |
epoch 20 |
epoch 30 |
epoch 40 |
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epoch 50 |
epoch 100 |
epoch 150 |
epoch 199 |
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Acknowledgement
This project is going with the GAN Theory and Practice part of the Deep Learning Course: from Algorithm to Practice.
Contacts
If you have any question about the project, please feel free to contact with me.
E-mail: [email protected]