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..
DehazeGAN: When Image Dehazing Meets Differential Programming
Authors: Hongyuan Zhu, Xi Peng, Vijay Chandrasekhar, Liyuan Li, Joo-Hwee Lim
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and realistic data show that our method outperforms state-of-the-art methods in terms of PSNR, SSIM, and subjective visual quality. |
| Researcher Affiliation | Academia | Hongyuan Zhu1, Xi Peng2 , Vijay Chandrasekhar1, Liyuan Li1, Joo-Hwee Lim1 1 Institute for Infocomm Research, A*STAR, Singapore 2 College of Computer Science, Sichuan University, China EMAIL, EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a repository was found. |
| Open Datasets | Yes | The dataset is synthesized using the indoor images from the SUN-RGBD dataset [Song et al., 2015], NYU-Depth dataset [Silberman et al., 2012] and natural images from the COCO dataset [Lin et al., 2014]. |
| Dataset Splits | Yes | After generating the hazy images, we randomly choose 85% data for training, 10% data for validation, and a small number of test images to form the indoor and outdoor subsets. |
| Hardware Specification | Yes | The entire network is trained on a Nvidia Titan X GPU in PyTorch. |
| Software Dependencies | No | The paper mentions 'PyTorch' and 'ADAM' but does not specify their version numbers or any other software dependencies with versions. |
| Experiment Setup | Yes | For training, we employ the ADAM [Kingma and Ba, 2015] optimizer with a learning rate of 0.002 and a batch size of eight. We set γ = 10 4 through the cross-validation. |