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..
Contrastive Label Enhancement
Authors: Yifei Wang, Yiyang Zhou, Jihua Zhu, Xinyuan Liu, Wenbiao Yan, Zhiqiang Tian
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on LDL benchmark datasets demonstrate the effectiveness and superiority of our method. |
| Researcher Affiliation | Academia | School of Software Engineering, Xi an Jiaotong University, Xi an, China |
| Pseudocode | Yes | Algorithm 1 The optimization of Con LE |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | SJAFFE dataset [Lyons et al., 1998] and SBU-3DFE dataset [Yin et al., 2006] are obtained from the two facial expression databases, JAFFE and BU-3DFE. ... Yeast datasets are derived from biological experiments on gene expression levels of budding yeast at different time points [Eisen et al., 1998]. |
| Dataset Splits | Yes | All algorithms are evaluated by ten times ten-fold cross-validation for fairness. |
| Hardware Specification | Yes | The code of this method is implemented by Py Torch [Paszke et al., 2019] on one NVIDIA Geforce GTX 2080ti GPU with 11GB memory. |
| Software Dependencies | No | The paper mentions "Py Torch [Paszke et al., 2019]" but does not specify a precise version number (e.g., 1.x.x) for reproducibility. It also mentions "SGD optimizer [Ruder, 2016]" and "Leaky Re LU activation function [Maas et al., 2013]" without version numbers. |
| Experiment Setup | Yes | When comparing with other algorithms, the hyperparameters of Con LE are set as follows: λ1 is set to 0.5, λ2 is set to 1 and the temperature parameter τI is 0.5. |