MDFL: Multi-Domain Diffusion-Driven Feature Learning
Authors: Daixun Li, Weiying Xie, Jiaqing Zhang, Yunsong Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |