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..
Exploiting Multi-Label Correlation in Label Distribution Learning
Authors: Zhiqiang Kou, Jing Wang, Jiawei Tang, Yuheng Jia, Boyu Shi, Xin Geng
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China 3 Chien-Shiung WU College, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | Yes | Algorithm 1 The pseudo-code for TLRLDL and TKLRLDL |
| Open Source Code | Yes | 1https://github.com/users/zhiqiang-kou/projects/1 |
| Open Datasets | Yes | Experimental Datasets: The experiments are conducted on 16 real-world datasets with label distribution. The key characteristics of these datasets are summarized in Table 1. Geng collects the first 12 datasets [Geng, 2016]. Among these, the first eight (from Spoem to Alpha) are from the clustering analysis of genome-wide expression in Yeast Saccharomyces cerevisiae [Eisen et al., 1998]. The SJAFFE is collected from JAFFE [Lyons et al., 1998], and the SBU 3DFE is obtained from BU 3DFE [Yin et al., 2006]. The Gene is obtained from the research on the relationship between genes and diseases [Yu et al., 2012]. The Scene consists of multi-label images, where the label distributions are transformed from rankings [Geng and Xia, 2014]. Besides, the SCUT-FBP, M2B, and fbp5500 are about facial beauty perception [Ren and Geng, 2017]. The last one, RAF-ML is a facial expression dataset [Li and Deng, 2019] with six-dimension expression distribution. |
| Dataset Splits | Yes | We run each method for ten-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters of the methods are as follows. The suggested parameters are used for IIS-LLD, EDL-LRL, LDLLC, and LDL-LCLR. For LDLLDM, λ1, λ2, and λ3 are tuned from 10-3, . . . , 103 , and g is tuned from 1 to 14. For Incom LDL, λ is selected from the range 2-10, . . . , 210 , and ρ = 1. For Adam-LDL-SCL, λ1, λ2, and λ3 are tuned from the set 10-3, . . . , 103 , and m is tuned from 0 to 14. For TLRLDL and TKLRLDL, α, λ are tuned from {0.005, 0.01, 0.05, 0.1, 0.5, 1, 10}, T is selected from 0.1 to 0.5, and k is tuned from 0 to m. |