Learning From Multi-Dimensional Partial Labels

Authors: Haobo Wang, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen, Gang Chen

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Comprehensive experiments on both synthetic and realworld datasets validate the effectiveness of our proposals.
Researcher Affiliation Academia 1Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University 2College of Computer Science and Technology, Zhejiang University 3School of Computer Science, Wuhan University
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating that the source code for the methodology is openly available.
Open Datasets Yes The MDC datasets are collected from UCI repository [Dheeru and Karra Taniskidou, 2017]: 1) Bridges estimates bridge properties from specific constraints; 2) WQplant and WQanimals determine the plant and animal species in Slovenian rivers; 3) Flare predicts number of times that certain types of solar flare occurred within 24 hours period; 4) Thyroid determines types of thyroid problems based on patient information.
Dataset Splits No The paper states, 'All the datasets are randomly split in to 80% training and 20% testing.' However, it does not explicitly mention a separate validation split.
Hardware Specification Yes All the computations are performed on a workstation with an i7-5930K CPU, a TITAN Xp GPU and 64GB main memory running Linux platform.
Software Dependencies No The paper mentions models like VGG-19 and methods like CLPL, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x).
Experiment Setup Yes The number of nearest neighbors is set as k = 10 for all the k NN-based approaches. Following the experimental setting in [Fang and Zhang, 2019], we set thr = 0.9 and α = 0.95 for P-VLS. Finally, following [Shen et al., 2018], the parameters of Co H are set as α = 100 and d = 10.