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..
Style-Content Metric Learning for Multidomain Remote Sensing Object Recognition
Authors: Wenda Zhao, Ruikai Yang, Yu Liu, You He
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four datasets show that our style-content metric learning achieves superior generalization performance against the state-of-the-art competitors. |
| Researcher Affiliation | Academia | 1Dalian University of Technology, Dalian, China 2Tsinghua University, Beijing, China |
| Pseudocode | No | The paper describes the framework and its components using text and equations, but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code and model are available at: https://github.com/wdzhao123/TSCM. |
| Open Datasets | Yes | We conduct experiments using four remote sensing datasets: NWPU (Cheng, Zhou, and Han 2016), DOTA (Xia et al. 2018), HRRSD (Zhang et al. 2019) and DIOR (Li et al. 2020c). |
| Dataset Splits | No | The paper describes using training and testing datasets but does not provide explicit details for a separate validation split (e.g., percentages or sample counts). |
| Hardware Specification | Yes | Our model is implemented by Pytorch on a PC with a NVIDIA RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Adam' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We resize the image size to 256 x 256 pixels and set the batch size to 36. Adam (Kingma and Ba 2014) is used as the optimizer, and learning rate is 1.25e-4. We exponentially decay the learning rate of each parameter group by gamma set as 0.99 every epoch. Bias ε is set to 1e-6 in case that divisor and square root turn zero. The hyper-parameters are set as α = 0.1, β = 0.5 and γ = 0.5. The model is firstly trained for 48 epochs and then the last four fully-connected layers are further finetuned for 10 epochs to improve performance. |