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 [1].
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. |