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What are diffusion models, and how do they differ from GANs?
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Answer Posted / Aditya Shakya
Diffusion models are a class of generative models that learn to model the gradual evolution of data over time. Unlike Generative Adversarial Networks (GANs), which work by iteratively improving the quality of generated samples, diffusion models simulate the reverse process by learning to start with noise and gradually refine it into realistic samples. This approach can lead to more stable training and better control over the generated data.
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