Bilevel Distance Metric Learning for Robust Image Recognition
Authors: Jie Xu, Lei Luo, Cheng Deng, Heng Huang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various occluded datasets demonstrate the effectiveness and robustness of our method. |
| Researcher Affiliation | Collaboration | 1 School of Electronic Engineering, Xidian University, Xi an, Shaanxi, China 2 Electrical and Computer Engineering, University of Pittsburgh, USA, 3 JDDGlobal.com |
| Pseudocode | Yes | Algorithm 1 Algorithm to solve Eq. (6) |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the source code for the described methodology. |
| Open Datasets | Yes | We conduct several experiments on three datasets, including NUST Robust Face database (NUST-RF) [2], OSR dataset [13] and Pub Fig database [6]. |
| Dataset Splits | Yes | For all metric learners, we use 5-fold cross validation and gauge the average accuracy and standard deviation as final performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | All the regularization parameters are tuned from range {10 4, 10 3, 10 2, 10 1, 1, 10, 102}. For CAP and FANTOPE methods, the parameter rank of distance matrix M is tuned from [10 : 5 : 30]. For a fair comparison, we specify 1 target neighbor for each training sample for all LMNN related methods. In testing phase, we use 1-NN method. |