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..
Cross-Scale Internal Graph Neural Network for Image Super-Resolution
Authors: Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of IGNN against the state-of-the-art SISR methods including existing non-local networks on standard benchmarks. |
| Researcher Affiliation | Collaboration | Shangchen Zhou1 Jiawei Zhang2 Wangmeng Zuo3 Chen Change Loy1 1Nanyang Technological University 2Sense Time Research 3Harbin Institute of Technology EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | https://github.com/sczhou/IGNN |
| Open Datasets | Yes | Following [23, 12, 45, 43, 5], we use 800 high-quality (2K resolution) images from DIV2K dataset [34] as training set. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions using DIV2K for training and evaluating on standard benchmarks, but no specific validation split information. |
| Hardware Specification | Yes | The IGNN is implemented on the Py Torch framework on an NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | We set the minibatch size to 4 and train our model using ADAM [18] optimizer with the settings of β1 = 0.9, β2 = 0.999, ϵ = 10 8. The initial learning rate is set as 10 4 and then decreases to half for every 2 105 iterations. Training is terminated after 8 105 iterations. The network is trained by using ℓ1 norm loss. |