Transfer Hashing with Privileged Information
Authors: Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | Institute of High Performance Computing, Singapore Nanyang Technological University, Singapore QCIS, University of Technology Sydney, Australia |
| Pseudocode | Yes | The procedure is summarized in Algorithm 1, and the details are described in this section. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | To verify the effectiveness of our proposed approaches, ITQ+ and Lap ITQ+, we conduct a series of experiments on three benchmark datasets: BBC Collection [Greene and Cunningham, 2006], multilingual Reuters [Amini et al., 2009], and NUS-WIDE [Chua et al., 2009]. |
| Dataset Splits | Yes | The parameters λ1 and λ2 for the proposed methods are tuned by cross validation in the range of [0, 0.001, 0.005, , 1, 2]. We set the maximum number of iterations to be 150. ... The results are averaged over 10 training-testing splits. ... We randomly select [10%, 30%, 50%, 70%] of the data from the target domain as the training set to evaluate the influence of training size on all the methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters λ1 and λ2 for the proposed methods are tuned by cross validation in the range of [0, 0.001, 0.005, , 1, 2]. We set the maximum number of iterations to be 150. ... We first evaluate the performance of different methods by varying the number of hashing bits in the range of {8, 16, 32, 64}, with fixed = 0.5. |