What are Diffusion Models? | Revolutionizing AI Creativity
https://youtu.be/ZTN2EgI251Q
Shima Shafiee
Adding Noise (Forward Process): They start with real data (like a real image) and gradually add random noise to it, step by step, until the image becomes pure noise. Removing Noise (Reverse Process): Then, they learn how to reverse this process — meaning, they start with random noise and gradually remove the noise to create a brand-new, realistic-looking sample (like a new image you've never seen before).
Diffusion Models are a type of machine learning model, mainly used to generate new data, like images, sounds, or even text.
They work in two main steps:
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