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..
Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network
Authors: Zhaoyang Wang, Dongyang Li, Mingyang Zhang, Hao Luo, Maoguo Gong
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically. |
| Researcher Affiliation | Collaboration | Zhaoyang Wang1 2*, Dongyang Li2 3, Mingyang Zhang1 , Hao Luo2 3, Maoguo Gong1, 1Ministry of Education, Key Laboratory of Collaborative Intelligence Systems, Xidian University 2DAMO Academy, Alibaba Group, 310023, Hangzhou, China 3Hupan Lab, 310023, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Testing process |
| Open Source Code | No | The paper does not include an unambiguous sentence stating that the authors are releasing their code or provide a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | In our experiments, we used three publicly available datasets to validate the performance of our model. These datasets include two remote-sensing HSI datasets: Pavia Center (Pavia C) dataset and Chikusei dataset(Yokoya and Iwasaki 2016), and one natural image HSI dataset: Harvard dataset (Chakrabarti and Zickler 2011). |
| Dataset Splits | No | The paper mentions using specific datasets for training and evaluation but does not specify exact train/validation/test splits, sample counts for each split, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and a 'pre-trained SR3 diffusion model' but does not provide specific version numbers for these or any other key software components (e.g., Python, PyTorch, CUDA versions) needed for reproducibility. |
| Experiment Setup | Yes | We used the Adam optimizer with β1 = 0.9 and β2 = 0.999 for training, with a batch size of 8 for the Harvard dataset and 4 for the Pavia C and Chikusei datasets. The learning rate was set to 1e 4 during GAE training and reduced to 1e 5 for the diffusion model. During the training process, we utilized a pre-trained SR3 diffusion model. In the GAE module, bands were divided into subgroups of size 16 for Pavia C and Chikusei datasets, and 8 for the Harvard dataset, with one-quarter overlap between subgroups. |