ICML 2024 "inverse problems" Papers
10 papers found
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
ICML 2024spotlightarXiv:2309.06642
D-Flow: Differentiating through Flows for Controlled Generation
Heli Ben-Hamu, Omri Puny, Itai Gat et al.
ICML 2024posterarXiv:2402.14017
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar et al.
ICML 2024posterarXiv:2402.12727
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai et al.
ICML 2024posterarXiv:2402.02149
Learning Pseudo-Contractive Denoisers for Inverse Problems
Deliang Wei, Peng Chen, Fang Li
ICML 2024posterarXiv:2402.05637
Plug-and-Play image restoration with Stochastic deNOising REgularization
Marien Renaud, Jean Prost, Arthur Leclaire et al.
ICML 2024posterarXiv:2402.01779
Prompt-tuning Latent Diffusion Models for Inverse Problems
Hyungjin Chung, Jong Chul YE, Peyman Milanfar et al.
ICML 2024posterarXiv:2310.01110
The Emergence of Reproducibility and Consistency in Diffusion Models
Huijie Zhang, Jinfan Zhou, Yifu Lu et al.
ICML 2024poster
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
ICML 2024spotlightarXiv:2311.09253
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee et al.
ICML 2024posterarXiv:2402.01052