Deep Incomplete Multi-View Learning Network with Insufficient Label Information
Authors: Zhangqi Jiang, Tingjin Luo, Xinyan Liang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the effectiveness of our DIMv LN, attaining noteworthy performance improvements compared to stateof-the-art competitors on several public benchmark datasets. |
| Researcher Affiliation | Academia | 1College of Science, National University of Defense Technology, Changsha 410073, Hunan, China 2Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, Shanxi, China |
| Pseudocode | Yes | Algorithm 1: Training Strategy of DIMv LN |
| Open Source Code | No | Code will be available at Git Hub. |
| Open Datasets | Yes | We conduct experiments on six public multi-view datasets as follows. Caltech101-20 : It contains 2,386 images of 20 objects. Following (Lin et al. 2022), we select HOG and GIST features as two views. CUB : It includes 11,788 samples belonging to 200 bird species. Following (Zhang et al. 2019), we select the top 10 bird species with two views. Wikipedia : It contains image and text features from 2,866 documents on 29 topics. Following (Wang, Yang, and Li 2016), the top 10 most popular topics are selected for our experiment. ALOI : It collects 110,250 images for 1,000 small objects. Following (Huang, Wang, and Lai 2023), we use a subset that contains 10,800 images of 100 objects with four views. Out-Scene : It contains 4,485 images of 15 scene categories. Following (Huang, Wang, and Lai 2023), we select 8 outdoor categories with total 2,688 images with four views. Animal 1 : It contains 50 animals of 30,475 images and we use the subset of 11,673 images from the first 20 animals with four views. |
| Dataset Splits | Yes | Each data can be split into training, validation, and test sets in the ratio of 7:1:2. |
| Hardware Specification | Yes | Our model is implemented by Py Torch on one NVIDIA Geforce A100 with GPU of 40GB memory. |
| Software Dependencies | No | The paper states, "Our model is implemented by Py Torch", but does not specify the version number of PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | Adam optimizer with the initial learning rate of 0.0001 is used for optimization of all datasets. The k-NN graphs are constructed using k-NN algorithm with Euclidean distance metric, where the neighbor number k is fixed to 10 for all datasets. In our experiments, we simply fix the α to 9. In our experiments, these two parameters [λ1 and λ2] are fixed to 1. |