Partial Label Learning by Semantic Difference Maximization
Authors: Lei Feng, Bo An
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Alibaba-NTU Singapore Joint Research Institute, Singapore |
| Pseudocode | Yes | Algorithm 1 The SDIM Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Dataset ecoli dermatology vehicle usps (Table 1), Lost 1122 108 16 2.23 automatic face naming [Panis and Lanitis, 2014] (Table 2) |
| Dataset Splits | Yes | For each dataset, we perform ten-fold cross-validation, and report the resulting mean prediction accuracies and the standard deviations. For all the approaches, parameters are selected by five-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For our approach, λ is searched in {0.001, 0.005, , 0.5} and β is searched in {0.00001, 0.00005, 0.0001, , 0.1}. For all the approaches, parameters are selected by five-fold cross-validation on the training set. |