Cat Faces

I created a Generative Adversarial Network (GAN) and trained it on a dataset of cat faces. This task provided hands-on experience in implementing and training GANs using PyTorch. I also implemented WGAN loss function and spectral normalization. What’s more I trained the model and visualized the output.

Key Features:

• Implemented a vanilla GAN and a Least Squares GAN (LSGAN).
• Trained the GAN on a dataset of cat faces.
• Visualized the output of the GAN every 1000 iterations and for the final model.
• Reported the discriminator and generator loss values every 1000 iterations.

Tools and Technologies:

• Python
• PyTorch

Source Code

The complete source code for this project is available on GitHub.