This blog post presents an interesting phenomenom that occurs in unconditional diffusion models used for inverse problem solving: a robustness to perturbations and noise to the input contexts. We claim that this phenomenom occurs due to the noisy context seen during training and show the results of some experiments on simple numerical trajectory examples that help justify this claim.

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