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Diffusion model

Technique for the generative modeling of a continuous probability distribution

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📌 Topics

  • Artificial Intelligence (3)
  • Machine Learning (2)
  • Diffusion Models (2)
  • AI Research (1)
  • Image Generation (1)
  • Autonomous driving technology (1)
  • Computer vision and AI (1)
  • Data augmentation techniques (1)
  • Bayesian Inference (1)
  • Structural Biology (1)
  • Scientific Computing (1)
  • AI in Medicine (1)

🏷️ Keywords

Diffusion models (7) · Computer vision (3) · Diffusion model (2) · Diffusion Transformer (1) · Dynamic Chunking (1) · Image Synthesis (1) · Computational Efficiency (1) · High-Resolution Generation (1) · DrivePTS (1) · Driving scene generation (1) · Autonomous driving (1) · Data augmentation (1) · Vision-Language Model (1) · Test-time guidance (1) · Bayesian posterior (1) · Miscalibrated inference (1) · Black hole image reconstruction (1) · Reward maximization (1) · Probability sampling (1) · CryoNet.Refine (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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Computer vision(3)Vehicular automation(1)Semantic change(1)Recommender system(1)Information retrieval(1)Large language model(1)Medical diagnosis(1)Reinforcement learning(1)Visual artifact(1)Machine learning(1)Diffusion model

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