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..
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
Authors: Wenlong Zhang, Xiaohui Li, Xiangyu Chen, Xiaoyun Zhang, Yu Qiao, Xiao-Ming Wu, Chao Dong
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Under SEAL, we benchmark existing real-SR methods, obtain new observations and insights into their performance, and develop a new strong baseline. In this work, we establish a systematic evaluation framework for real-SR, namely SEAL, which assesses relative, distributed, and overall performance rather than relying solely on absolute, average, and misleading evaluation strategy commonly used in current evaluation methods. Section 5 is titled EXPERIMENTS and contains numerous tables and figures showing empirical results. |
| Researcher Affiliation | Academia | 1The Hong Kong Polytechnic University, 2Shanghai AI Laboratory 3Shanghai Jiao Tong University 4University of Macau 5Shenzhen Institute of Advanced Technology, CAS |
| Pseudocode | Yes | Algorithm 1 Image degradation clustering |
| Open Source Code | Yes | The source code is available at https://github.com/XPixelGroup/SEAL |
| Open Datasets | Yes | We take Set14 (Zeyde et al., 2010) and DIV2K val (Lim et al., 2017) to construct the test sets for systematic evaluation, denoted as Set14-SE and DIV2K val-SE, respectively. We use the 100 representative degradation parameters to synthesize 100 training datasets based on DIV2K. |
| Dataset Splits | Yes | We take Set14 (Zeyde et al., 2010) and DIV2K val (Lim et al., 2017) to construct the test sets for systematic evaluation, denoted as Set14-SE and DIV2K val-SE, respectively. we randomly add degradations to images in the DIV2K (Agustsson & Timofte, 2017) validation set to construct a single real-DIV2K val set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models, or cloud resources. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not specify versions for any programming languages, libraries, or other software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | The models within the model zoo are initially pre-trained under the real-SR setting. Subsequently, they undergo a fine-tuning process consisting of a total of 2 × 10^5 iterations. The Adam (Kingma & Ba, 2014) optimizer with β1 = 0.9 and β2 = 0.99 is used for training. The initial learning rate is 2 × 10^-4. We adopt L1 loss to optimize the networks. |