Optimizing each diffusion sampling step separately, with sparse deviations and an optional projected-gradient outer loop, reduces memory and improves reconstruction on some inverse problems, though gains are inconsistent.
(23) Moreover, since g1 is L1-Lipschitz continuous, if M is a (δ/L1)-net of X2 + Bn 1 (r), we have that g1(M ) is a δ-net of g1 X2 + Bn 1 (r)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
Optimizing each diffusion sampling step separately, with sparse deviations and an optional projected-gradient outer loop, reduces memory and improves reconstruction on some inverse problems, though gains are inconsistent.