Lemon-cmd/Diffusion-Models-and-Associative-Memory
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
This project helps researchers and machine learning practitioners analyze how diffusion models learn to generate new images based on what they've been trained on. You input trained diffusion models and the original training images. The output helps you classify the model's generated images into categories like "memorized," "spurious," or "generalized," and provides insights into the model's learning process. This is ideal for those studying the fundamental properties of generative AI models.
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Use this if you are a machine learning researcher studying how diffusion models learn and generalize, and you need to categorize and analyze the output images of these models.
Not ideal if you are looking for a tool to simply train or deploy diffusion models for practical image generation without deep analysis of their learning mechanisms.
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8
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Language
Python
License
MIT
Category
Last pushed
Jun 04, 2025
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