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..
Learning a Spiking Neural Network for Efficient Image Deraining
Authors: Tianyu Song, Guiyue Jin, Pengpeng Li, Kui Jiang, Xiang Chen, Jiyu Jin
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct comprehensive experiments on commonly used benchmark datasets to evaluate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1Dalian Polytechnic University 2Nanjing University of Science and Technology 3Harbin Institute of Technology |
| Pseudocode | No | The information is insufficient. The paper provides architectural diagrams but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code source is available at https://github.com/Ming Tian99/ESDNet. |
| Open Datasets | Yes | We retrained all models on the four publicly available datasets (Rain12 [Li et al., 2016], Rain200L [Yang et al., 2017], Rain200H [Yang et al., 2017], Rain1200 [Zhang and Patel, 2018]) to ensure a fair comparison of all methods. |
| Dataset Splits | No | The information is insufficient. While the paper specifies training and test data sizes for datasets like Rain200L/H ('1800 synthetic rain images for training, along with 200 images designated for testing'), it does not explicitly mention or provide details for a separate validation split used in its experiments. |
| Hardware Specification | Yes | All experiments are executed on an NVIDIA Ge Force RTX 3080Ti GPU (12G). |
| Software Dependencies | No | The information is insufficient. The paper mentions the 'Py Torch framework' but does not specify its version or the versions of any other software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | During the training process, we conduct the proposed network in the Py Torch framework with an Adam optimizer and a batch size of 12. We set the learning rate to 1 10 3 and apply the cosine annealing strategy [Song et al., 2023a] to steadily decrease the ο¬nal learning rate to 1 10 7. For Rain200L, Rain200H, and Rain1200 datasets, we train the model by 1000 epochs. We set the stacking numbers of SRB to [4,4,8] in the encoder stage and [2,2] in the decoder stage. For the Ξ± of the gradient proxy function, it is set to 4 according to [Su et al., 2023]. |