Online Heterogeneous Transfer Metric Learning
Authors: Yong Luo, Tongliang Liu, Yonggang Wen, Dacheng Tao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in diverse applications demonstrate both effectiveness and efficiency of the proposed method. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Nanyang Technological University, Singapore 2 UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 The proposed online heterogeneous transfer metric learning (OHTML) algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of its source code or links to a code repository. |
| Open Datasets | Yes | This set of experiments is conducted on the popular Caltech101 [Fei-Fei et al., 2004] dataset... We conduct scene clustering on the Scene-15 [Lazebnik et al., 2006] dataset... we employ the well-known labeled face in the wild (LFW) [Huang et al., 2007] dataset... and the Corel5K [Duygulu et al., 2002] dataset is used for evaluation. |
| Dataset Splits | No | For the Caltech-101 dataset: "Half of the dataset is used for training and the remaining is for test." For Scene-15: "We randomly split the dataset into equal size for training and test." For LFW: "We adopt the standard 10-folds split of the dataset [Huang et al., 2007], and each fold is used for test in turn." For Corel5K: "half of the data are used for training." While hyperparameters are tuned, the paper does not specify a distinct validation set split for this tuning for all datasets or in general. |
| Hardware Specification | Yes | The algorithms are implemented using Matlab and the experiments are conducted on a 3.4 GHz Intel Xeon E5-2687W (8 cores) computer. |
| Software Dependencies | No | The algorithms are implemented using Matlab. The paper mentions Matlab but does not provide a specific version number, nor does it list any other software dependencies with version numbers. |
| Experiment Setup | Yes | The candidate set for choosing the trade-off hyper-parameters is {10i|i = 5, , 4} if unspecified in the original papers. The hyper-parameters γA and γI are tuned on the set {10i|i = 5, , 4} and {10i|i = 2, , 7} respectively. ... We run the experiments ten times by randomly choosing the labeled set. |