Multi-level Generative Models for Partial Label Learning with Non-random Label Noise
Authors: Yan Yan, Yuhong Guo
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on both synthesized and real-world partial label datasets. The proposed approach demonstrates the state-of-the-art performance for partial label learning. |
| Researcher Affiliation | Academia | Yan Yan 1 , Yuhong Guo2,3 1Northwestern Polytechnical University, China 2Carleton University, Canada 3Canada CIFAR AI Chair, Amii |
| Pseudocode | No | The paper describes an algorithm verbally in Section 3.4, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | The synthetic datasets are generated from six UCI datasets, ecoli, deter, vehicle, segment, satimage and letter. ... We used five real-world PL datasets that are collected from several application domains, including FG-NET [Panis and Lanitis, 2014] for facial age estimation, Lost [Cour et al., 2011], Yahoo! News [Guillaumin et al., 2010] for automatic face naming in images or videos, MSRCv2 [Dietterich and Bakiri, 1994] for object classification, and Bird Song [Briggs et al., 2012] for bird song classification. |
| Dataset Splits | Yes | For each PL dataset, ten-fold cross-validation is performed and the average test accuracy results are recorded. ... For each dataset, ten-fold cross-validation is conducted. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the general architecture and optimization method but does not provide specific hyperparameter values or detailed training configurations (e.g., learning rate, batch size, number of epochs). |