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Diffusion model

Technique for the generative modeling of a continuous probability distribution

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  • Artificial Intelligence (4)
  • Machine Learning (3)
  • Computer Vision (2)
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Diffusion models (3) · arXiv (2) · Latent reward modeling (1) · Vision-Language Models (1) · Flow-matching (1) · Preference optimization (1) · Generative AI (1) · ArXiv (1) · Bird-SR (1) · Super-resolution (1) · Reward Feedback Learning (1) · Image processing (1) · Machine learning (1) · Meta-modeling (1) · LLM activations (1) · Residual stream (1) · Neural network analysis (1) · Mechanistic interpretability (1) · DECO (1) · Diffusion Transformer (1)

📖 Key Information

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset.

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