Large-scale Distance Metric Learning with Uncertainty
Large-scale Distance Metric Learning with Uncertainty Abstract 现有的大多数DML方法都采用点对约束或者三元组约束，在这种情况下约束的数量是原始样本的平方或者三次方，因此处理大规模数据集具有挑战性。此外，真实世界的数据可能包含各种不确定性，特别是对于图像数据。不确定性可能会误导训练过程并导致模型性能下降。通过观察图像数据，作者发现可
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相关文章
- 1. 【2017_ICCV】Deep Metric Learning with Angular Loss
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