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..
Trusted Multi-View Classification
Authors: Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results validate the effectiveness of the proposed model in accuracy, reliability and robustness. |
| Researcher Affiliation | Academia | Zongbo Han, Changqing Zhang , College of Intelligence and Computing Tianjin University Tianjin, China EMAIL Huazhu Fu Inception Institute of Artificial Intelligence Abu Dhabi, UAE EMAIL Joey Tianyi Zhou Institute of High Performance Computing A*STAR, Singapore EMAIL |
| Pseudocode | Yes | The optimization process for the proposed model is summarized in Algorithm 1 (in the Appendix). |
| Open Source Code | No | No statement regarding the release of open-source code for the described methodology or a link to a code repository was found. |
| Open Datasets | Yes | In this section, we conduct experiments on six real-world datasets: Handwritten1, CUB (Wah et al., 2011), Caltech101 (Fei-Fei et al., 2004), PIE2, Scene15 (Fei-Fei & Perona, 2005) and HMDB (Kuehne et al., 2011). |
| Dataset Splits | Yes | The training, validation and test set is set to 8:1:1 respectively for all datasets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were found in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were found. |
| Experiment Setup | Yes | For all datasets, we train the network for 200 epochs using Adam (Kingma & Ba, 2014) optimizer with an initial learning rate of 0.001. The learning rate is decayed by a factor of 0.1 every 50 epochs. The weight decay parameter for all the methods is set to 5 × 10−4. The balance factor λt in Eq. 10 is set to 0.001 at the beginning, and is gradually increased by multiplying by 10/200 for each epoch, with the maximum value set to 1.0. |