Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multi-Level Metric Learning via Smoothed Wasserstein Distance
Authors: Jie Xu, Lei Luo, Cheng Deng, Heng Huang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on four standard databases show that our method obviously outperforms other state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 School of Electronic Engineering, Xidian University, Xi an 710071, China 2 Electrical and Computer Engineering, University of Pittsburgh, PA, 15261, USA |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm for solving Problem (13) |
| Open Source Code | No | No explicit statement or link is provided for open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We use two challenge person re-identification datasets at multi-shot scenario, i.e., PRID 2011 dataset [Hirzer et al., 2011] and i LIDSVID dataset [Office, 2008]. Kin Face W-II dataset... [Lu et al., 2014]. Traffic video database... [Chan and Vasconcelos, 2005]. |
| Dataset Splits | Yes | Experiment Settings: In the experiment, we split each dataset into two folds. In each time, one fold of data is for training and the other fold is used as testing data. As a benchmark for comparison, we use the pre-specified training/testing split, which is generated for 5-fold cross validation [Lu et al., 2014]. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running the experiments are mentioned. |
| Software Dependencies | No | No specific software dependencies, including library names with version numbers, are mentioned. |
| Experiment Setup | Yes | In our method, we set ρ0 = 1, and ρt = 1 C , t = 1, , C. For LMNN with capped trace norm and Fantope norm methods, the regularization parameters are tuned from range {10 4, 10 3, 10 2, 10 1, 1, 10, 102}, and parameter rank of matrix M is from [30 : 5 : 70]. |