Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning
Authors: Zhen-Ru Zhang, Qian-Wen Zhang, Yunbo Cao, Min-Ling Zhang10973-10980
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative studies against state-of-the-art approaches clearly show the effectiveness of the proposed unlabeled data exploitation strategy for multi-class semi-supervised learning. |
| Researcher Affiliation | Collaboration | Zhen-Ru Zhang1,2,3, Qian-Wen Zhang3, Yunbo Cao3, Min-Ling Zhang1,2,4* 1 School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3 Tencent Cloud Xiaowei, Beijing, China 4 Collaborative Innovation Center of Wireless Communications Technology, China zhangzr@seu.edu.cn, {cowenzhang, yunbocao}@tencent.com, zhangml@seu.edu.cn |
| Pseudocode | Yes | Table 1: The pseudo-code of EUPAL. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of its methodology. |
| Open Datasets | Yes | In this paper, a total of 13 benchmark multi-class data sets (Dua and Graff 2017) have been employed for experimental studies whose characteristics are summarized in Table 2. |
| Dataset Splits | No | The paper defines training and test sets but does not explicitly mention a 'validation' set or its split for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions software like LIBSVM and IPAL as being used but does not provide specific version numbers for them or any other software dependencies. |
| Experiment Setup | Yes | As shown in Table 1, the values of k (number of nearest neighbors), α (balancing parameter), and T (maximum number of iterations) for EUPAL are set to be 5, 0.4 and 50 respectively. Furthermore, the supervised training algorithm L and the partial label training algorithm P are instantiated with LIBSVM and IPAL accordingly. |