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!