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..
Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution
Authors: Zihang Liu, Zhenyu Zhang, Hao Tang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both real-world and synthetic datasets demonstrate that SAMSR significantly improves perceptual quality and detail recovery, particularly in semantically complex images. |
| Researcher Affiliation | Academia | 1Beijing Institute of Technology 2Nanjing University 3School of Computer Science, Peking University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training the Pixel-wise Sampling Framework |
| Open Source Code | Yes | Our code is released at https://github.com/Liu-Zihang/SAMSR. |
| Open Datasets | Yes | For real-world evaluation, we utilize Real SR [Cai et al., 2019b] and Real Set65 [Yue et al., 2024]. For synthetic datasets, we follow the standard pipeline to create LR inputs from 3000 HR images randomly selected from Image Net [Wang et al., 2024]. |
| Dataset Splits | No | For synthetic datasets, we follow the standard pipeline to create LR inputs from 3000 HR images randomly selected from Image Net [Wang et al., 2024]. The paper does not explicitly provide details on how these images were split into training, validation, or test sets for their experiments. |
| Hardware Specification | Yes | Table 5: A comparison of the training time cost and results on NVIDIA RTX4090. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, our model achieves convergence in only 10,000-15,000 iterations. The hyper-parameter m controls the noise addition speed and intensity for pixels with different levels of semantic richness during the forward diffusion process. ... Therefore, in this paper, we set m to 1/5. ... where ΜΈ is a hyper-parameter that controls the contribution of the semantic consistency loss. |