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 | Archive PDF | Plain Text | 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 κ2 {0.5, 1.5, 2.5}, s {0, 100, 200, 333}, and ρ {0.5, 0.75}. Table 3: LD-SMC hyperparameters for all tasks. |