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..
Unfolding the Alternating Optimization for Blind Super Resolution
Authors: zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at much higher speed. |
| Researcher Affiliation | Academia | Zhengxiong Luo1,2,3, Yan Huang1,2, Shang Li2,3, Liang Wang1,4,5, and Tieniu Tan1,4 1 Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) 2 Institute of Automation, Chinese Academy of Sciences (CASIA) 3 School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) 4 Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) 5 Chinese Academy of Sciences, Artificial Intelligence Research (CAS-AIR) |
| Pseudocode | No | The paper provides architectural diagrams of the network modules but no pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/greatlog/DAN.git. |
| Open Datasets | Yes | We collect 3450 HR images from DIV2K [1] and Flickr2K [11] as training set. |
| Dataset Splits | No | The paper details training and testing data but does not explicitly provide information on validation set splits or its usage in the training process. |
| Hardware Specification | Yes | All models are trained on RTX2080Ti GPUs. |
| Software Dependencies | No | The paper mentions software like Adam for optimization but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The input size during training is 64 64 for all scale factors. The batch size is 64. Each model is trained for 4 10^5 iterations. We use Adam [22] as our optimizer, with β1 = 0.9, β2 = 0.99. The initial learning rate is 2 10^4, and will decay by half at every 1 10^5 iterations. |