Stanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion Models
The lecture introduces Diffusion Models as a predominant class of generative models used for tasks like image, video, and action generation. The core mechanism involves two processes: the forward process (gradually adding Gaussian noise to an original clean image $X_0$ over time steps $T$) and the reverse process (learning to iteratively denoise the noisy data back to a clean state). Training these models is achieved by applying the Evidence Lower Bound (ELBO) framework, which minimizes the Kullback-Leibler (KL) divergence between the true conditional distribution and the parameterized model's prediction.
Key takeaways
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Diffusion Models Overview
These models are generative frameworks that can be applied to images, videos, and robotics actions. They are noted as a superior approach compared to GANs and Variational Autoencoders (VAEs) for image generation.
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Forward vs. Reverse Process
4:05
The forward process is a fixed, known noising process that transforms $X_0$ to noise $X_T$. The reverse process is the learned component, parameterized by $ heta$, which predicts $p_ heta(X_{t-1}|X_t)$—the step-by-step denoising path.
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Training Objective (ELBO)
6:25
The goal is to maximize the marginal likelihood $ ext{log } p_ heta(X_0)$ by applying the Evidence Lower Bound (ELBO). This involves minimizing the KL divergence between the true posterior $q(X_{t-1}|X_t, X_0)$ and the parameterized model's distribution.
