On the Stability and Generalization of Triplet Learning
Authors: Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then obtain the excess risk bounds of the order O(n 1 2 logn) for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n 1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. |
| Researcher Affiliation | Academia | Jun Chen1, Hong Chen2, 3, 4, Xue Jiang5, Bin Gu6, Weifu Li2, 3, 4, Tieliang Gong7, 8, Feng Zheng5 1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 2College of Science, Huazhong Agricultural University, Wuhan 430070, China 3Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan 430070, China 4Key Laboratory of Smart Farming for Agricultural Animals, Wuhan 430070, China 5Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China 6Mohamed bin Zayed University of Artiļ¬cial Intelligence, Abu Dhabi, United Arab Emirates 7School of Computer Science and Technology, Xi an Jiaotong University, Xi an 710049, China 8Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Ministry of Education, Xi an 710049, China |
| Pseudocode | No | The paper describes algorithmic steps and equations, such as the SGD update rule in (3), but does not include a clearly labeled pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with actual datasets. It refers to a 'training set S' in a theoretical definition, but no specific publicly available dataset is mentioned or linked for training. |
| Dataset Splits | No | The paper is theoretical and does not describe any empirical experiments, thus no validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments, therefore no specific software dependencies or versions are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no specific experimental setup details like hyperparameters or training configurations are provided. |