Curvilinear Distance Metric Learning
Authors: Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the synthetic and real-world datasets validate the superiority of our method over the state-of-the-art metric learning models. |
| Researcher Affiliation | Collaboration | S. Chen, J. Yang, and C. Gong are with the PCA Lab, Key Lab of Intelligent Perception and Systems for High Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China (E-mail: {shuochen, csjyang, chen.gong}@njust.edu.cn). L. Luo and H. Huang are with the Electrical and Computer Engineering, University of Pittsburgh, and also with JD Finance America Corporation, USA (E-mail: lel94@pitt.edu, henghuanghh@gmail.com). J. Li is with the Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA (E-mail: junli@mit.edu). |
| Pseudocode | Yes | Algorithm 1 Solving Eq. (11) via Stochastic Gradient Descent. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | The datasets are from the well-known UCI machine learning repository [1] including MNIST, Autompg, Sonar, Australia, Hayes-r, Glass, Segment, Balance, Isolet, and Letters. |
| Dataset Splits | No | The paper describes training and test splits (e.g., '60% of all data is randomly selected for training, and the rest is used for test.' or '80% of examples are randomly selected as the training examples, and the rest are used for testing.'), but it does not explicitly specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions certain methods and components used (e.g., 'k-NN classifier', 'DSIFT', 'Siamese-CNN'), but it does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, scikit-learn 0.24). |
| Experiment Setup | Yes | In our experiments, the parameters λ and c are fixed to 1.2 and 10, respectively. The SGD parameters h and ρ are fixed to 10^3 and 10^-3, respectively. |