Chapter08

Name		Name	Last commit message	Last commit date
parent directory ..
data/mnist		data/mnist
8.04 Demystifying GAN Loss Function.ipynb		8.04 Demystifying GAN Loss Function.ipynb
8.05 Generating images using GAN in TensorFlow.ipynb		8.05 Generating images using GAN in TensorFlow.ipynb
8.07 Implementing DCGAN to Generate CIFAR Images.ipynb		8.07 Implementing DCGAN to Generate CIFAR Images.ipynb
8.09 Building LSGAN in TensorFlow.ipynb		8.09 Building LSGAN in TensorFlow.ipynb
README.md		README.md

README.md

8.1. Distinguishing generative and discriminative models
8.2. Say hello to GANs
- 8.2.1. Breaking down the generator
- 8.2.2. Breaking down the discriminator
- 8.2.3. How do they learn?
8.3. Architecture of GANs
8.4. Demystifying GAN loss function
- 8.4.1. Discriminator Loss
- 8.4.2. Generator Loss
- 8.4.3. Total Loss
- 8.4.4. Heuristic Loss
8.5. Generating images using GAN in TensorFlow
8.6. DCGAN - Adding convolution to the GAN
- 8.6.1. Deconvolutional Generator
- 8.6.2. Convolutional Discriminator
8.7. Implementing DCGAN to generate CIFAR images
8.8. Least Squares GAN
8.9. Building LSGAN in tensorflow
8.10. WGAN - GANs with Wasserstein distance
- 8.10.1. Are we just minimizing JS divergence in GANs?
- 8.10.2. What is Wasserstein distance?
- 8.10.3. Demystifying K-Lipschitz function
- 8.10.4. Loss function of WGAN
- 8.10.5. WGAN in Tensorflow