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 | Conference PDF | Archive PDF | Plain Text | 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. |