Privileged label enhancement with multi-label learning
Authors: Wenfang Zhu, Xiuyi Jia, Weiwei Li
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, comparison experiments on 12 datasets demonstrate that our proposal can better fit the ground-truth label distributions. |
| Researcher Affiliation | Academia | 1Key Laboratory of Information Perception and Systems for Public Security of MIIT, Nanjing University of Science and Technology, China 2Jiangsu Key Lab of Image and Video Understanding for Social Security, Nanjing University of Science and Technology, China 3State Key Laboratory for Novel Software Technology, Nanjing University, China 4College of Astronautics, Nanjing University of Aeronautics and Astronautics, China |
| Pseudocode | Yes | Algorithm 1 The PLEML algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology or include links to code repositories. |
| Open Datasets | Yes | There are 12 real-world label distribution datasets in our experiments, including two facial expression datasets SJAFFE [Lyons et al., 1998] and SBU 3DFE [Yin et al., 2006], ten biological experiments datasets Yeast [Eisen et al., 1998]. |
| Dataset Splits | Yes | For simplicity, we divide the datasets into 4/5 training set and 1/5 testing set. Therefore, after 5-fold cross-validation, we can get the prediction value of each instance. For the second group of experiments, 10 times 10 fold cross-validation is employed for each dataset and the average results are recorded. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and optimization methods (e.g., L-BFGS, ADMM, LDL-SCL) and their parameters, but it does not specify software names with their version numbers (e.g., Python, PyTorch, specific libraries) that would be needed to reproduce the experiments. |
| Experiment Setup | Yes | For PLEML, the values of the parameters λ1 and λ2 are selected among {2 4, 2 3, , 28}, and γ = 0.1, C = 0.1. For LDL-SCL algorithm, the parameters are set to λ1 = 0.001, λ2 = 0.001, and m = 5. |