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..
Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification
Authors: Fengxiang Yang, Ke Li, Zhun Zhong, Zhiming Luo, Xing Sun, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, Shaozi Li12597-12604
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed framework can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy. |
| Researcher Affiliation | Collaboration | 1Artificial Intelligence Department, Xiamen University, China 2Post Doctoral Mobile Station of Information and Communication Engineering, Xiamen University, China 3Tencent Youtu Lab, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Procedure of the proposed method. Inputs: Labeled source dataset S, unlabeled target dataset T , Image Net pre-trained model M. Training epochs e1, e2 and e3. Maximum round r2, r3. Outputs: Adapted model Mada. |
| Open Source Code | Yes | Our code is available at https://github.com/Flying Roast Duck/ACT AAAI20. |
| Open Datasets | Yes | We conduct experiments on three large-scale benchmark datasets: Market-1501 (Zheng et al. 2015), Duke MTMCre ID (Ristani et al. 2016; Zheng, Zheng, and Yang 2017) and CUHK03 (Li et al. 2014). |
| Dataset Splits | No | We conduct experiments on three large-scale benchmark datasets: Market-1501 (Zheng et al. 2015), Duke MTMCre ID (Ristani et al. 2016; Zheng, Zheng, and Yang 2017) and CUHK03 (Li et al. 2014). The m AP and rank-1 accuracy are adopted as evaluation metrics. We use the new-protocol proposed in (Zhong et al. 2017) for evaluating CUHK03. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions "Image Net pre-trained Res Net-50 model" but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Adam solver is used to optimize the re-ID model with an initial learning rate of 3 10 4. We train re-ID model for 150 epochs and the learning rate is linearly decreased to 0 for the last 50 epochs. Margin m in the triplet loss is set to 0.3. Training batch size Bs = 64. Input images are resized to 128 64. We also use random flip and random erasing (Zhong et al. 2020) for data argumentation. In the clustering-based adaptation stage, we constrain the minimum size of a cluster to 4 and set density radius p = 1.6 10 3. After a clustering step, we train the model for 30 epochs, and iterate this procedure for 30 rounds. In the last asymmetric co-teaching stage, we form triplet samples in a batch to compute triplet loss for each anchor image. Anchors with the smallest K% losses are selected for further training. We set the small loss ratio K = 20% and linearly increase it to 100% for the whole Rco epochs, Rco = 10. Adam is used to fine-tune the models for 10 epoch with a fixed learning rate of 6 10 5. |