- Week 1: Intro to GANs
- See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
- Week 2: Deep Convolutional GANs
- Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!
- Week 3: Wasserstein GANs with Gradient Penalty
- Learn advanced techniques to reduce instances of GAN failure due to imbalances between the generator and discriminator! Implement a WGAN to mitigate unstable training and mode collapse using W-Loss and Lipschitz Continuity enforcement.
- Week 4: Conditional GAN & Controllable Generation
- Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories!
