Large-scale Distance Metric Learning with Uncertainty
Large-scale Distance Metric Learning with Uncertainty Abstract 現有的大多數DML方法都採用點對約束或者三元組約束，在這種情況下約束的數量是原始樣本的平方或者三次方，因此處理大規模數據集具有挑戰性。此外，真實世界的數據可能包含各種不確定性，特別是對於圖像數據。不確定性可能會誤導訓練過程並導致模型性能下降。通過觀察圖像數據，作者發現可
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相關文章
- 1. 【2017_ICCV】Deep Metric Learning with Angular Loss
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