OpenViewer: Openness-Aware Multi-View Learning
Authors: Shide Du, Zihan Fang, Yanchao Tan, Changwei Wang, Shiping Wang, Wenzhong Guo
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | 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, ξ = 5, and λ1 and λ2 selected from {10 3, 5 10 3, , 100}. |