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..
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. |