Generate Cartoon Images using DC-GAN
Deep Convolutional GAN is a generative adversarial network architecture. It uses a couple of guidelines, in particular:
- Replacing any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
- Using batchnorm in both the generator and the discriminator.
- Removing fully connected hidden layers for deeper architectures.
- Using ReLU activation in generator for all layers except for the output, which uses tanh.
- Using LeakyReLU activation in the discriminator for all layer.
Checkout the detailed explanation of AvatarGAN in the article AvatarGAN
- Define Generator and Discriminator network architecture
- Train the Generator model to generate the fake data that can fool Discriminator
- Train the Discriminator model to distinguish real vs fake data
- Continue the training for several epochs and save the Generator model
Cartoon Set which is a collection of random 2D cartoon avatar images. Download the dataset using the shell script.
sh download-dataset.sh
This will download the dataset in data/ directory.
If you want to train the model in Google Colab, upload the dataset folder to Google Drive. The destination path should be projects/cartoons/.
Check out the model being trained to generate cartoon images.
