Deep Embedded Complementary and Interactive Information for Multi-View Classification
Authors: Jinglin Xu, Wenbin Li, Xinwang Liu, Dingwen Zhang, Ji Liu, Junwei Han6494-6501
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several public datasets demonstrate the rationality and effectiveness of our method. |
| Researcher Affiliation | Collaboration | 1Northwestern Polytechnical University, China, 2Nanjing University, China 3National University of Defense Technology, China, 4Xidian University, China, 5Kwai Inc. |
| Pseudocode | No | The paper does not contain a specific pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the methodology is available. |
| Open Datasets | Yes | Caltech101/20. The dataset (Fei-Fei, Fergus, and Perona 2007) ... AWA. This dataset (Lampert, Nickisch, and Harmeling 2009) ... NUSOBJ. This is a subset of NUS-WIDE (Chua et al. 2009) ... Reuters. It (Amini, Usunier, and Goutte 2009) ... Hand. This dataset (Dheeru and Karra Taniskidou 2017) |
| Dataset Splits | Yes | Referring to (Andrew et al. 2013; Wang et al. 2015), we split each dataset into three parts: 70% samples for training, two-thirds of the rest samples for validation, and one-third of that for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types) used for running the experiments. It only mentions 'trained by Adam' and 'large batchsize' generally. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam' but does not provide specific version numbers for any software components. It only says 'trained by Adam with batch normalization'. |
| Experiment Setup | Yes | All the networks in this paper are trained by Adam with batch normalization, where the learning rate is 10 3, β1 = 0.5, β2 = 0.9. In addition, we study the impact of batch size on the classification performance of our Mv NNcor by setting batch size like 32, 64, 128, and 256 respectively. ... Each of fv is a fully-connected network which consists of dv input units and two hidden layers with 400 and 200 units equipped with Re LU activation function. ψ consists of 2002 input units and 200 hidden units with Re LU activation function. φ consists of 200 M input units and 300 hidden units with Re LU activation function, followed by a linear output layer with C units. |