Learn the Highest Label and Rest Label Description Degrees
Authors: Jing Wang, Xin Geng
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis shows the generalization of LDL-HR. Besides, the experimental results on 18 real-world datasets validate the statistical superiority of our method. |
| Researcher Affiliation | Academia | Jing Wang and Xin Geng MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {wangjing91, xgeng}@seu.edu.cn |
| Pseudocode | No | The paper describes the optimization process and gradient calculation but does not include a formally labeled "Algorithm" or "Pseudocode" block. |
| Open Source Code | Yes | Available at: https://github.com/wangjing4research/LDL HR |
| Open Datasets | Yes | Table 1 summarizes the statistics of the experimental datasets. The first 15 datasets (from Alpha to SBU 3DFE) are collected by Geng [2016]. The last three datasets M2B [Nguyen et al., 2012], SCUT-FBP [Xie et al., 2015], and fbo5500 [Liang et al., 2018] are about facial beauty perception. |
| Dataset Splits | Yes | We first tune the parameters of each method by 10-fold crossvalidation, and then run each method with the best parameters for 10 times random data partitions (90% for training and 10% for testing). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions applying a quasi-Newton algorithm L-BFGS, but it does not specify any software libraries or dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For LDL-HR, λ1 = 0.001, λ2 and λ3 are tuned from the candidate set {10 3, , 1}, and ρ = 0.01. We first tune the parameters of each method by 10-fold crossvalidation, and then run each method with the best parameters for 10 times random data partitions (90% for training and 10% for testing). |