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.
(20) Based on Lemma A.1 and Assumptions 4.1 and 4.2, we present the proof of Theorem 4.4 as follows
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.