Generative Models
GANs, diffusion models, and image generation.
Autoencoders and VAEs
Autoencoders learn compressed latent representations by encoding inputs and reconstructing them, while Variational Autoencoders add a probabilistic structure that enables principled generation of new data.
Diffusion Models
Diffusion models generate images by learning to reverse a gradual noising process, iteratively denoising random Gaussian noise into coherent images, and have dethroned GANs as the dominant paradigm for image synthesis.
GAN Training Dynamics
Training GANs is notoriously unstable due to the adversarial minimax objective, with mode collapse and oscillation as primary failure modes, mitigated by architectural and loss function innovations.
Generative Adversarial Networks
GANs pit a generator network against a discriminator network in a minimax game, producing remarkably realistic synthetic images when the two reach equilibrium.
Image Inpainting
Image inpainting fills in missing or masked regions of an image with plausible content, using contextual reasoning from surrounding pixels through techniques ranging from partial convolutions to diffusion-based generation.
Image Super-Resolution
Image super-resolution recovers high-resolution detail from low-resolution inputs, evolving from simple CNN upscaling (SRCNN) through GAN-based perceptual methods (SRGAN) to robust real-world models (Real-ESRGAN).
Image-to-Image Translation
Image-to-image translation learns mappings between visual domains – from sketches to photos, day to night, horses to zebras – using paired supervision (Pix2Pix) or unpaired cycle-consistency constraints (CycleGAN).
Latent Diffusion and Stable Diffusion
Latent diffusion models run the diffusion process in a compressed latent space rather than pixel space, dramatically reducing computational cost and enabling practical high-resolution text-to-image generation as realized in Stable Diffusion.
Neural Style Transfer
Neural style transfer separates the content and style of images using CNN feature representations – content captured by activation patterns, style captured by Gram matrices – enabling artistic rendering of photographs in the style of any painting.
StyleGAN
StyleGAN introduces a style-based generator architecture that injects learned styles at each resolution through adaptive instance normalization, enabling unprecedented control over face synthesis at 1024x1024 resolution.