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Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
As an effective way to learn a distance metric between pairs of samples, deep metric learning (DML) has drawn significant attention in recent years. The key idea of DML is to learn a set of hierarchical nonlinear mappings using deep neural networks, and then project the data samples into a new feature space for comparing or matching. Although DML has achieved practical success in many applications, there is no existing work that theoretically analyzes the generalization error bound for DML,
doi:10.24963/ijcai.2019/352
dblp:conf/ijcai/HuaiXMYSCZ19
fatcat:4alzfyqeb5gjrcigiyfuuimswe