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..
Unsupervised Multi-Exposure Image Fusion Breaking Exposure Limits via Contrastive Learning
Authors: Han Xu, Liang Haochen, Jiayi Ma
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative, quantitative, and ablation experiments validate the superiority and generalization of MEF-CL. Our code is publicly available at https://github.com/hanna-xu/MEF-CL. |
| Researcher Affiliation | Academia | Electronic Information School, Wuhan University, Wuhan 430072, China |
| Pseudocode | No | The paper describes network architectures and processes but does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/hanna-xu/MEF-CL. |
| Open Datasets | Yes | We conduct experiments on the SICE dataset (Cai, Gu, and Zhang 2018)1 and perform the verification on different scenes, including indoor and outdoor scenes.1https://github.com/csjcai/SICE |
| Dataset Splits | Yes | We randomly selected 479 image sequences as the training set. The remaining 80 image sequences are as the test set. |
| Hardware Specification | Yes | The experiments are performed on an NVIDIA Geforce GTX Titan V GPU. Traditional methods are tested on a laptop with 3.2 GHz AMD Ryzen 7 5800H CPU. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | Yes | The hyper-parameters are set as: λ1 = 10, λ2 = 20, τ = 0.01. The batch size is set to 20, the training epoch is 2, and the learning rate is 0.0001. We use the RMSProp optimizer for optimization. |