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..
Singe Image Rain Removal with Unpaired Information: A Differentiable Programming Perspective
Authors: Hongyuan Zhu, Xi Peng, Joey Tianyi Zhou, Songfan Yang, Vijay Chanderasekh, Liyuan Li, Joo-Hwee Lim9332-9339
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on public benchmark demonstrates our promising performance compared with nine state-of-the-art methods in terms of PSNR, SSIM, visual qualities and running time. |
| Researcher Affiliation | Collaboration | 1Institute for Infocomm Research, A*STAR, Singapore, 2College of Computer Science, Sichuan University, China 3Institute of Performance Computing, A*STAR, Singapore 4AI Lab, TAL Education Group, China |
| Pseudocode | No | The paper describes the model architecture and processes using natural language and mathematical equations, but it does not provide any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about the release of source code or a link to a code repository. |
| Open Datasets | Yes | We use Rain800 (Zhang and Patel 2018) for benchmarking. The Rain800 dataset contains 700 synthesized images for training and 100 images for testing using randomly sampled outdoor images. |
| Dataset Splits | Yes | The Rain800 dataset contains 700 synthesized images for training and 100 images for testing using randomly sampled outdoor images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions that 'The entire network is trained using the Pytorch framework.' but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Adam is used as optimization algorithm with a mini-batch size of 1. The learning rate starts from 0.001. The models are trained for up to 10 epochs to ensure convergence. We use a weight decay of 0.0001 and a momentum of 0.9. The entire network is trained using the Pytorch framework. During training, we set γ = 1. |