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..
Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification
Authors: Zheng Wang, Mang Ye, Fan Yang, Xiang Bai, Shin'ichi Satoh
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 National Institute of Informatics, Japan 2 Hong Kong Baptist University, China 3 The University of Tokyo, Japan 4 Huazhong University of Science of Technology, China |
| Pseudocode | No | The paper includes architectural diagrams (e.g., Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | Following [Wang et al., 2016b], the evaluation is run on two simulated person datasets SALR-VIPe R and SALR-PRID, which are based on the VIPe R dataset [Gray et al., 2007] and the PRID450S dataset [Roth et al., 2014] respectively, and the public CAVIAR dataset [Cheng et al., 2011]. |
| Dataset Splits | Yes | Following [Wang et al., 2016b], all datasets are randomly divided into training set and testing set. Persons for training and testing are respectively 532 and 100 (SALR-VIPe R), 400 and 50 (SALR-PRID), and 44 and 10 (CAVIAR). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions deep learning models like ResNet-50 and VGG network but does not specify any software libraries or their version numbers (e.g., TensorFlow, PyTorch, Python versions). |
| Experiment Setup | Yes | The training process includes the following three steps: (1) We first initialize the re-identification network separately. We choose Res Net-50 [He et al., 2016] as the base. The Res Net50 is pre-trained with Image Net [Russakovsky et al., 2015], and then fine-tuned with the Market-1501 [Zheng et al., 2015] dataset. (2) The cascaded generator networks are initialized with MSE losses. (3) The whole network is trained simultaneously with all the losses. ... following [Ledig et al., 2016], we set α = 2 * 10^-6 and β = 1 * 10^-3. |