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..
MDFL: Multi-Domain Diffusion-Driven Feature Learning
Authors: Daixun Li, Weiying Xie, Jiaqing Zhang, Yunsong Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on three multi-modal remote sensing datasets show that MDFL reaches an average overall accuracy of 98.25%, outperforming various state-of-the-art baseline schemes. Code available at https://github.com/LDXDU/ MDFL-AAAI-24. |
| Researcher Affiliation | Academia | State Key Laboratory of Integrated Services Networks, Xidian University, Xi an 710071, China |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Code available at https://github.com/LDXDU/ MDFL-AAAI-24. |
| Open Datasets | Yes | To validate the effectiveness of the proposed method in analyzing high-dimensional data, three remote sensing multimodal datasets with hyperspectra, namely the Houston2013 dataset, the Trento dataset, and the MUUFL dataset, are selected for verification of the proposed classification model. |
| Dataset Splits | No | The paper mentions "training samples" and "test images" but does not explicitly describe a validation dataset split, specific percentages for validation, or how a validation set was used. |
| Hardware Specification | Yes | These experiments are conducted on a machine equipped with an NVIDIA A100 Tensor Core GPU. |
| Software Dependencies | No | The paper mentions the use of "Adam optimizer" and a "step scheduler" but does not specify version numbers for any software dependencies like deep learning frameworks or libraries. |
| Experiment Setup | Yes | The training samples are randomly cropped to a size of 7 × 7. The Adam optimizer is employed with an initial learning rate set to 1e-3, and a weight decay of 5e-3 was applied. The training process spans 1000 epochs. In addition, a step scheduler with a step size of 50 and gamma value of 0.9 is utilized. The batch size is set to 64. Additionally, for incorporating noise, a total step size of 500 was used, and the values of t = 0, 50, 100, 200, 400 are selected. |