Reverse diffusion is a concept often discussed in the context of generative models, particularly in machine learning and statistical physics. In simple terms, it refers to a process in which a system moves from a state of higher disorder or uncertainty (often represented by noise) to a state of lower disorder or higher structure, effectively "reversing" the natural process of diffusion.
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