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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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]. |