Description
Chair: Laurence Perreault Levasseur
Generative modeling for high-dimensional data, such as images and audio, is extremely challenging due to the curse of dimensionality. To overcome this difficulty, we introduce a homotopic approach inspired by numerical equation solving, which involves designing a homotopy of probability distributions that smoothly progresses from simple noise distribution to complex data distribution. I will present two families of approaches that rely on such homotopies: score-based diffusion models and consistency models. Both approaches use a differential equation to convert data to noise and learn to estimate the time reversal with deep neural networks. These models allow for flexible neural networks, enable zero-shot image editing, and generate high-quality samples that achieve state-of-the-art performance in many generative modeling benchmarks.
