Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
A closer look on Deepfakes: face sythesis with StyleGAN, face swap with XceptionNet and facial attributes and expression manipulation with StarGAN
The sixth article-series of GAN in computer vision - we explore semantic image synthesis and learning a generative model from a single image
The fifth article-series of GAN in computer vision - we discuss self-supervision in adversarial training for unconditional image generation as well as in-layer normalization and style incorporation in high-resolution image synthesis.
The fourth article-series of GAN in computer vision - we explore 2K image generation with a multi-scale GAN approach, video synthesis with temporal consistency, and large-scale class-conditional image generation in ImageNet.
The third article-series of GAN in computer vision - we encounter some of the most advanced training concepts such as Wasserstein distance, adopt a game theory aspect in the training of GAN, and study the incremental/progressive generative training to reach a megapixel resolution.
The second article of the GANs in computer vision series - looking deeper in generative adversarial networks, mode collapse, conditional image synthesis, and 3D object generation, paired and unpaired image to image generation.
The first article of the GANs in computer vision series - an introduction to generative learning, adversarial learning, gan training algorithm, conditional image generation, mode collapse, mutual information
What's the difference of generative and discriminative models and what is a GAN
Learn what autoencoders are and build one to generate new images