Adaptively Unified Semi-supervised Learning for Cross-Modal Retrieval
Authors: Liang Zhang, Bingpeng Ma, Jianfeng He, Guorong Li, Qingming Huang, Qi Tian
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Wiki, Pascal and NUS-WIDE datasets show that the proposed method outperforms the state-of-the-art methods even when we set 20% samples without class labels. |
| Researcher Affiliation | Academia | 1 University of Chinese Academy of Sciences, Beijing, 100049, China 2 Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS, Beijing, 100190, China 3 Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing, 100190, China 4 Department of Computer Science, University of Texas at San Antonio, TX, 78249, USA |
| Pseudocode | No | The paper describes the optimization algorithm using mathematical equations, but it does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Wiki dataset is collected from Wikipedia feature articles [Rasiwasia et al., 2010]. Pascal dataset consists of 5,011/4,952(training/testing) images-tag pairs [Everingham et al., 2010]. NUS-WIDE dataset consists of 40,834/27,159 (training/testing) image-tag pairs, which are pruned from the training-test split of the NUS dataset [Chua et al., 2009] |
| Dataset Splits | Yes | On this dataset, we randomly select 2,000 pairs of the data for training and 866 pairs for testing. parameters are set by 5-fold cross validation on the training set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions models like word2vec, CNN (Caffe), and t-SNE, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | After cross validation, the parameters s and λ of AUSL are set to 2 and 0.1 in all the experiments. The dimension of the common subspace is set to 10, 20 and 10 for Wiki, Pascal and NUS-WIDE, respectively. |