generative-models
Autoencoders and VAEs: teaching a model to compress the world Lesson
An autoencoder is a network trained to squeeze data through a narrow bottleneck and rebuild it, and a variational autoencoder makes that bottleneck a smooth space of probabilities you can sample from, which is why nearly every modern image and video generator does its work inside one.
Vector Quantization: Turning Continuous Data Into a Vocabulary Lesson
Vector quantization forces a neural network's continuous internal representations to snap to a finite set of learned reference vectors, converting images, audio, or video into sequences of discrete symbols that a language model can predict just like words.
Training on the best of K guesses is a third scaling axis alongside parameters and data News
A paper from UIUC and Harvard shows that generating several candidate outputs per training example and learning only from the closest match improves sample efficiency 6.2-fold, and that the benefit grows rather than shrinks as models and datasets get bigger.
Neural text-to-speech: how a model turns writing into a voice Lesson
Neural text-to-speech converts written text into audio in three stages - working out the sounds, deciding how long each one lasts, and generating the actual waveform - and the last stage, the vocoder, is where most of the model's size and difficulty hides.
Diffusion models: how AI turns noise into images and video Lesson
Diffusion models generate images and video by starting from pure random noise and removing it step by step until a coherent picture emerges -- the technique behind Stable Diffusion, Sora, and interactive video systems like Vidu S1.
GANs: the two-network duel that taught AI to imagine Lesson
A generative adversarial network trains two neural networks against each other -- a forger trying to create fake data and a detective trying to spot it -- until the forger's output becomes indistinguishable from the real thing, the breakthrough that first made AI image generation convincing.
Flow matching: how AI learns to turn noise into a picture Lesson
Flow matching teaches an AI to generate images by learning a smooth flow that carries random noise, step by step, into a realistic picture -- a cleaner, faster successor to diffusion that powers modern image models like FLUX.
An image generator that catches and corrects its own errors mid-draw News
Image-generating models often quietly break the very rule they were told to follow. A new method trains them to notice that error as they work and steer back on target.