Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
Authors: Guile Wu, Shaogang Gong2898-2906
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on ten Re-ID benchmarks show that Fed Re ID achieves compelling generalisation performance beyond any locally trained models without using shared training data, whilst inherently protects the privacy of each local client. |
| Researcher Affiliation | Academia | Guile Wu, Shaogang Gong Queen Mary University of London guile.wu@qmul.ac.uk, s.gong@qmul.ac.uk |
| Pseudocode | No | The paper includes equations and describes steps, but does not present a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used four large-scale Re-ID datasets (Duke MTMC-Re ID (Zheng, Zheng, and Yang 2017), Market-1501 (Zheng et al. 2015), CUHK03 (Li et al. 2014; Zhong et al. 2017) and MSMT17 (Wei et al. 2018)) as nonshared local datasets in four client sites... The Fed Re ID model was then evaluated on five smaller Re-ID datasets (VIPe R (Gray and Tao 2008), i LIDS (Zheng, Gong, and Xiang 2009), 3DPe S (Baltieri, Vezzani, and Cucchiara 2011), CAVIAR (Cheng et al. 2011) and GRID (Loy, Liu, and Gong 2013)), plus a large-scale Re-ID dataset (CUHK-SYSU person search (Xiao et al. 2017)) as new unseen target domains for out-of-the-box deployment tests. ... Besides, we used CIFAR-10 (Krizhevsky and Hinton 2009) for federated formulation generalisation analysis on image classification. |
| Dataset Splits | No | The paper mentions "10 training/testing splits" and discusses evaluation metrics and hyperparameters, but does not explicitly specify a validation set split or methodology for hyperparameter tuning using a validation set. For example, it says "We empirically set batch size to 32..." which implies empirical setting rather than validation set optimization. |
| Hardware Specification | Yes | Our models were implemented with Python(3.6) and Py Torch(0.4), and trained on TESLA V100 GPU (32GB). |
| Software Dependencies | Yes | Our models were implemented with Python(3.6) and Py Torch(0.4), and trained on TESLA V100 GPU (32GB). |
| Experiment Setup | Yes | We empirically set batch size to 32, maximum global communication epochs kmax=100, maximum local steps tmax=1, and temperature T=3. We used SGD as the optimiser with Nesterov momentum 0.9 and weight decay 5e-4. The learning rates were set to 0.01 for embedding networks and 0.1 for mapping networks, which decayed by 0.1 every 40 epochs. |