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..
Safe Multi-View Deep Classification
Authors: Wei Liu, Yufei Chen, Xiaodong Yue, Changqing Zhang, Shaorong Xie
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on various kinds of multi-view datasets validate that the proposed SMDC method can achieve precise and safe classification results. In this section, we extensively evaluate the proposed method on real-world multi-view datasets and compare it with existing multi-view classification methods. |
| Researcher Affiliation | Collaboration | 1 College of Electronics and Information Engineering, Tongji University, Shanghai, China 2 School of Computer Engineering and Science, Shanghai University, Shanghai, China 3 Artificial Intelligence Institute of Shanghai University, Shanghai, China 4 College of Intelligence and Computing, Tianjin University, Tianjin, China 5 VLN Lab, NAVI Med Tech Co., Ltd. Shanghai, China |
| Pseudocode | Yes | Algorithm 1: Algorithm for Safe Multi-View Deep Classification (SMDC) |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on six real-world multi-view datasets as follows: Handwritten (Van Breukelen et al. 1998), Scene15 (Fei Fei and Perona 2005), Animal (Lampert, Nickisch, and Harmeling 2013), Caltech101 (Fei-Fei, Fergus, and Perona 2004), CUB (Wah et al. 2011) and HMDB (Kuehne et al. 2011). |
| Dataset Splits | Yes | We then use 5-fold cross-validation to select the learning rate from 1e 4, 3e 4, 1e 3, 3e 3 . For all datasets, 20% samples are used as test sets. |
| Hardware Specification | Yes | The model is implemented by Py Torch on one NVIDIA A100 with GPU of 40GB memory. |
| Software Dependencies | No | The paper mentions 'The model is implemented by Py Torch', but it does not specify a version number for PyTorch or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | The Adam optimizer (Kingma and Ba 2014) is used to train the network, where l2-norm regularization is set to 1e 5. We then use 5-fold cross-validation to select the learning rate from 1e 4, 3e 4, 1e 3, 3e 3 . |