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..
SECRET: Self-Consistent Pseudo Label Refinement for Unsupervised Domain Adaptive Person Re-identification
Authors: Tao He, Leqi Shen, Yuchen Guo, Guiguang Ding, Zhenhua Guo879-887
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets show the superiority of our method. Specifically, our method outperforms the state-ofthe-arts by 6.3% in terms of m AP on the challenging dataset MSMT17. In the purely unsupervised setting, our method also surpasses existing works by a large margin. |
| Researcher Affiliation | Collaboration | Tao He*1,2, Leqi Shen*1,2, Yuchen Guo 2, Guiguang Ding 1,2, Zhenhua Guo3 1 School of Software, Tsinghua University, Beijing, China 2 Beijing National Research Center for Information Science and Technology (BNRist) 3 Alibaba Group |
| Pseudocode | Yes | Algorithm 1: Mutual refinement of pseudo labels Algorithm 2: Noisy instance elimination |
| Open Source Code | Yes | Code is available at https://github.com/Lunar Shen/SECRET. |
| Open Datasets | Yes | The proposed SECRET is evaluated on the popular benchmark datasets: Market-1501 (Zheng et al. 2015), Duke MTMC-re ID (Ristani et al. 2016) and MSMT17 (Wei et al. 2018). |
| Dataset Splits | No | The paper discusses training on source domains and fine-tuning on target domains for unsupervised domain adaptation. It mentions evaluation on benchmark datasets (Market-1501, Duke MTMC-re ID, MSMT17). However, it does not explicitly describe a separate validation dataset split with specific percentages or counts used during the training process for hyperparameter tuning or early stopping, distinct from the final test/evaluation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using "Res Net-50 as our backbone," which is a model architecture, but it does not specify any software dependencies (e.g., programming languages, deep learning frameworks like PyTorch or TensorFlow, or other libraries) along with their version numbers. |
| Experiment Setup | Yes | The input images are resized to 256 × 128. Random flip, padding, and random crop are used as data augmentation in both source domain pre-training and target domain fine-tuning. Random erase (Zhong et al. 2020a) is only used in target domain finetuning. We randomly sample 4 instances per ground truth (in pre-training) or pseudo label (in fine-tuning) in a mini-batch, resulting in batch size 64. In pre-training, the initial learning rate is set to 3.5 × 10−4, and decays by 0.1 at 40 and 70 epoch, and 80 epochs in total. In fine-tuning, clustering-andpseudo-label-fine-tuning runs 80 epochs in total. The learning rate is set to 3.5 × 10−4. The hyper-parameters K in filtering pseudo labels of global and local features is set to be 40%. |