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 [1].
Cross-View Feature Learning for Scalable Social Image Analysis
Authors: Wenxuan Xie, Yuxin Peng, Jianguo Xiao
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, the performance of our proposed method is evaluated on two real-world datasets in image classification. |
| Researcher Affiliation | Academia | Wenxuan Xie and Yuxin Peng and Jianguo Xiao Institute of Computer Science and Technology, Peking University, Beijing 100871, China |
| Pseudocode | No | The paper describes the CVFL framework and its solution mathematically but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions running MATLAB codes but does not provide concrete access to source code for the methodology described, nor does it state that the code is publicly available. |
| Open Datasets | Yes | We conduct experiments on two publicly-available datasets in the multi-class classification setting. The first dataset is Corel-5K (Corel for short) (Duygulu et al. 2002), which is composed of 50 categories... The second dataset is NUS-WIDE-Object (NUS for short) (Chua et al. 2009), which is collected from the photo sharing website Flickr. |
| Dataset Splits | Yes | Following the partition of the dataset, 4,500 images are used for training and the rest are used for test. ...we follow the standard train/test partition of the dataset, where the training set and the test set contain 14,270 and 9,683 images, respectively. ...Parameters are determined by 5-fold cross-validation in the experiments. |
| Hardware Specification | Yes | Note that we run MATLAB codes on a server with 2.20GHz CPU and 128GB RAM. |
| Software Dependencies | No | The paper mentions 'MATLAB codes' and 'linear SVM (Fan et al. 2008)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Parameters are determined by 5-fold cross-validation in the experiments. We empirically find that the performance of CVFL reaches its peak when both λ and γ are chosen between 1 and 2. Concretely, we choose λ = 1.5 and γ = 1.8 for the Corel dataset, and choose λ = 1.7 and γ = 1.9 for the NUS dataset. ...we adopt linear SVM (Fan et al. 2008) to obtain the classification accuracy for evaluation. |