Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
Support both GPU and CPU.
- Vanilla GAN (GAN)
- Conditonal GAN (cGAN)
- Improved Conditonal GAN (Improved cGAN)
- Deep Convolutional GAN (DCGAN)
- Wasserstein GAN (WGAN)
- Improved Training of Wasserstein GAN (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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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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epoch 0 | epoch 10 | epoch 20 | epoch 30 | epoch 40 |
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epoch 50 | epoch 100 | epoch 150 | epoch 199 | - |
- |
epoch 0 | epoch 10 | epoch 20 | epoch 30 | epoch 40 |
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epoch 50 | epoch 60 | epoch 70 | epoch 80 | epoch 90 |
epoch 0 | epoch 10 | epoch 20 | epoch 30 | epoch 40 |
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epoch 50 | epoch 100 | epoch 150 | epoch 199 | - |
- |
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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This project is going with the GAN Theory and Practice part of the Deep Learning Course: from Algorithm to Practice.
If you have any question about the project, please feel free to contact with me.
E-mail: guan.wang0706@gmail.com