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..
OpenViewer: Openness-Aware Multi-View Learning
Authors: Shide Du, Zihan Fang, Yanchao Tan, Changwei Wang, Shiping Wang, Wenzhong Guo
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments and Studies Datasets, Compared Methods, and Evaluation Metric. We conduct experiments in challenging open-environment classification tasks under six well-known multi-view datasets. |
| Researcher Affiliation | Academia | 1 College of Computer and Data Science, Fuzhou University, Fuzhou, China 2 Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China 3 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology, Jinan, China |
| Pseudocode | Yes | Open Viewer can be summarized as Algorithm 1 in Appendix. |
| Open Source Code | Yes | Code https://github.com/dushide/Open Viewer |
| Open Datasets | Yes | We conduct experiments in challenging open-environment classification tasks under six well-known multi-view datasets. This includes two scenarios: 1) Animals, AWA, NUSWIDEOBJ, and VGGFace2-50 datasets contain different manual and deep features; 2) ESP-Game and NUSWIDE20k datasets include various vision and language features. The statistics of these datasets are summarized in Table 1 (details in Appendix). |
| Dataset Splits | Yes | Meanwhile, the dataset is partitioned as follows: 10% of the known class samples are allocated for training, another 10% for validation, and the rest 80% for testing. |
| Hardware Specification | Yes | Open Viewer is implemented using the Py Torch on an NVIDIA Ge Force RTX 4080 GPU with 16GB of memory. |
| Software Dependencies | No | The paper mentions "Py Torch" as a software used, but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | We train Open Viewer for 100 epochs with a batch size of 50, a learning rate of 0.01, ΞΎ = 5, and Ξ»1 and Ξ»2 selected from {10 3, 5 10 3, , 100}. |