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..
Disentangled Feature Learning Network for Vehicle Re-Identification
Authors: Yan Bai, Yihang Lou, Yongxing Dai, Jun Liu, Ziqian Chen, Ling-Yu Duan
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets. We conduct experiments on Vehicle ID [Liu et al., 2016a], Ve RI-776 [Liu et al., 2016c] and VERI-Wild [Lou et al., 2019b] datasets, which are widely used vehicle Re ID benchmarks. |
| Researcher Affiliation | Academia | 1 National Engineering Lab for Video Technology, Peking University, Beijing, China 2 ISTD Pillar, Singapore University of Technology and Design, Singapore 3 Peng Cheng Laboratory, Shenzhen, China |
| Pseudocode | No | The paper describes algorithms verbally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of its methodology. |
| Open Datasets | Yes | We conduct experiments on Vehicle ID [Liu et al., 2016a], Ve RI-776 [Liu et al., 2016c] and VERI-Wild [Lou et al., 2019b] datasets, which are widely used vehicle Re ID benchmarks. |
| Dataset Splits | No | The paper mentions 'In training stage' and 'During testing' but does not explicitly provide specific percentages or counts for training, validation, and test splits for the datasets used in a comprehensive manner for reproducibility. It only lists test sizes for some datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Res Net50 [He et al., 2016]' as the backbone, but does not specify any software dependencies (e.g., Python version, deep learning framework, libraries) with version numbers. |
| Experiment Setup | Yes | Regarding parameters, we set ω as 0.5 and triplet margin as 0.6 in metric learning following [Lou et al., 2019b], and λ = 0.5 in hybrid ranking. The models are trained for 50 epochs. Learning rate starts from 0.003. The size of the input image is 256 256. |