Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Authors: Idan Achituve, Hai Victor Habi, Amir Rosenfeld, Arnon Netzer, Idit Diamant, Ethan Fetaya
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on Image Net and FFHQ show the benefits of LDSMC over competing methods in various inverse problem tasks and especially in challenging inpainting tasks. |
| Researcher Affiliation | Collaboration | 1 Sony Semiconductor Israel (SSI), Israel 2Faculty of Engineering, Bar-Ilan University, Israel. Correspondence to: Idan Achituve <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 LD-SMC |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated LD-SMC on Image Net (Russakovsky et al., 2015) and FFHQ (Karras et al., 2019); both are common in the literature of inverse problems |
| Dataset Splits | Yes | We sampled 1024 random images from the validation set of each dataset which were used to evaluate all methods. |
| Hardware Specification | Yes | The experiments were carried out mainly using an NVIDIA A100 having 40GB and 80GB memory. |
| Software Dependencies | No | The paper mentions using specific models and samplers like DDIM, VQ-4 / CIN256-V2, but does not provide specific version numbers for software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The guidance scale was fixed to 1.0 in all our experiments. For all methods, we performed a hyperparameter search on η {0.05, 0.5, 1.0} and found that LD-SMC worked best with η = 1.0. For our method, we also performed a grid search over Îș2 {0.5, 1.5, 2.5}, s {0, 100, 200, 333}, and Ï {0.5, 0.75}. Table 3: LD-SMC hyperparameters for all tasks. |