Soft Diffusion: Score Matching for General Corruptions. (arXiv:2209.05442v2 [cs.CV] UPDATED)

We define a broader family of corruption processes that generalizes
previously known diffusion models. To reverse these general diffusions, we
propose a new objective called Soft Score Matching that provably learns the
score function for any linear corruption process and yields state of the art
results for CelebA. Soft Score Matching incorporates the degradation process in
the network. Our new loss trains the model to predict a clean image,
\textit{that after corruption}, matches the diffused observation. We show that
our …

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