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.
How a team from the University of Freiburg and CARIAD SE built a two-stage diffusion system that doesn’t just predict where vehicles will go—it asks “what if the ego vehicle chose differently?” and forecasts how every surrounding agent would react.
Give a neurologist a brain scan from today and ask what it will look like in five years, and you are asking for something the field has wanted for a long time. Not a population average, not a risk score,…
latent diffusion model: Predicting Alzheimer’s Brain MRI Change With BrLP Read More »
Diffuse gliomas make up roughly 80 percent of malignant brain tumors and a large share of primary brain tumor deaths, and glioblastoma, the most aggressive subtype, still carries a median survival under 15 months after diagnosis despite decades of treatment…
A Diffusion Model That Paints What Your Glioma Will Look Like Next Year Read More »
Visual emotion analysis is the task of looking at an image and predicting the dominant emotion it conveys, sadness, amusement, awe, disgust, and so on. It sounds close to ordinary image classification, but the paper’s introduction is careful to draw…
How EmoVLM-KD Blends Vision Models Into an Emotion Reading VLM Read More »
Retinal fundus photographs are one of the more quietly useful tools in modern medicine. A single photograph of the back of the eye, taken through the pupil with no incision and no injection, can reveal signs of diabetic retinopathy, but…
Generative Models: A Causal Fix for Shortcut Learning in Retinal AI Models Read More »