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..
Pixel-Aware Deep Function-Mixture Network for Spectral Super-Resolution
Authors: Lei Zhang, Zhiqiang Lang, Peng Wang, Wei Wei, Shengcai Liao, Ling Shao, Yanning Zhang12821-12828
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three benchmark HSI datasets demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Collaboration | 1Inception Institute of Artificial Intelligence, United Arab Emirates 2School of Computer Science, Northwestern Polytechnical University, China 3School of Computing and Information Technology, University of Wollongong, Australia |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code or links to a code repository. |
| Open Datasets | Yes | In this study, we adopt three benchmark HSI datasets, including NTIRE (Timofte et al. 2018), CAVE (Yasuma et al. 2010) and Harvard (Chakrabarti and Zickler 2011). |
| Dataset Splits | No | The paper specifies training and testing sets but does not explicitly mention a separate validation set or cross-validation for hyperparameter tuning. |
| Hardware Specification | No | The paper states it was 'implemented... on the Pytorch platform' but does not provide any specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions 'Pytorch platform (Ketkar 2017)' but does not provide a specific version number for PyTorch or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In the proposed method, we adopt 4 FM blocks (i.e., including Fc and p=3). Each block contains n = 3 basis functions. The basis functions and the mixing functions consist of m=2 convolutional blocks. Each convolutional block contains 64 filters. In each FM block, basis functions are equipped with 3 different-sized filters for convolution, i.e., 3 3, 7 7 and 11 11. While the filter size in all other convolutional blocks is fixed as 3 3. ... In the training stage, we employ the Adam optimizer (Kingma and Ba 2014) with the weight decay 1e-6. The learning rate is initially set as 1e-4 and halved in every 20 epochs. The batch size is 128. We terminate the optimization at the 100-th epoch. |