GANs, diffusion models, and the architectures behind modern generative AI. Research-grade explainers on how generative models learn, where they fail, and the design choices that separate state-of-the-art methods from the rest, each tied to the paper it came from.
Text to motion systems have gotten genuinely good at turning a sentence like walk forward and turn right into a plausible looking animated character. The trouble shows up at the edges of what language is good for. A prompt like…
DrawMotion Turns a Doodle Into 3D Character Motion Read More »
Interest in RNA structure has been climbing for a reason that has nothing to do with academic curiosity. RNA based therapeutics are now a real category of medicine, and designing an RNA molecule with a specific function starts with knowing…
How Base Pair Conditioning Lets RNAbpFlow Skip the MSA Read More »
All in one image restoration is the idea of training a single network that can fix rain, haze, low light, snow, blur, or noise without needing a separate specialist model for each. It works well when a test image genuinely…
How Stochastic Transport Fixes Composite Image Restoration Read More »
RigFace edits expression, pose, and lighting in portraits while preserving identity, by injecting identity features through self-attention instead of cross-attention in a diffusion model.
Why RigFace Injects Identity Through Self-Attention Instead of Cross-Attention Read More »
DiffFuseNet runs diffusion denoising on shallow encoded features instead of full images, making infrared-visible fusion roughly ten times faster than prior diffusion methods without sacrificing quality.
A research grade look at how GANs learn, the minimax game, the math behind the optimal discriminator, why training breaks, and a full PyTorch DCGAN.
How Generative Adversarial Networks Actually Work Read More »
A research team from the Chinese University of Hong Kong and Florida State University has delivered the first unified convergence theory for a broad class of score based generative models in 2-Wasserstein distance, and it shows that the forward process…
Wasserstein Convergence Guarantees for Score-Based Generative Models Read More »
Tianyi Lin, Chi Jin, and Michael I. Jordan from Columbia, Princeton, and UC Berkeley deliver the first rigorous nonasymptotic analysis of two-timescale gradient descent ascent — the algorithm that has powered GAN training in practice for years — establishing tight…
TTGDA: Two-Timescale Gradient Descent Ascent for Nonconvex Minimax Optimization Read More »
Researchers from TU Munich and Imperial College London built a latent diffusion model that generates deformation fields — not intensities — to morph a general population brain atlas into condition-specific ones, producing sharper, more anatomically faithful, and fully interpretable atlases…
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Brain Atlases Read More »
Every night, weather satellites go partially blind — their visible cameras shut off the moment the sun dips below the horizon. Researchers at Ocean University of China built a reparameterized GAN that reads three thermal infrared channels and reconstructs what…
RepVIS-GAN: Nighttime Satellite Visible Image Retrieval from Infrared Data Read More »