Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate the incorporation of additional hyperparameters, notably the threshold, which defines whether the sample pair is informative. The threshold provides a stable numerical standard for determining whether to retain the pairs. It is a vital parameter to reduce the redundant sample pairs participating in training. Nonetheless, finding the optimal threshold can be a time-consuming endeavor, often requiring extensive grid searches. Because the threshold cannot be dynamically adjusted in the training stage, we should conduct plenty of repeated experiments to determine the threshold. Therefore, we introduce a novel approach for adjusting the thresholds associated with both the loss function and the sample mining strategy. We design a static Asymmetric Sample Mining Strategy (ASMS) and its dynamic version Adaptive Tolerance ASMS (AT-ASMS), tailored for sample mining methods. ASMS utilizes differentiated thresholds to address the problems (too few positive pairs and too many redundant negative pairs) caused by only applying a single threshold to filter samples. AT-ASMS can adaptively regulate the ratio of positive and negative pairs during training according to the ratio of the currently mined positive and negative pairs. This meta-learning-based threshold generation algorithm utilizes a single-step gradient descent to obtain new thresholds. We combine these two threshold adjustment algorithms to form the Dual Dynamic Threshold Adjustment Strategy (DDTAS). Experimental results show that our algorithm achieves competitive performance on CUB200, Cars196, and SOP datasets.

翻译：损失函数和样本挖掘策略是深度度量学习算法中的核心组成部分。然而，现有损失函数或挖掘策略往往需要引入额外的超参数，尤其是用于判断样本对是否具有信息量的阈值。阈值为判定是否保留样本对提供了稳定的数值基准，是减少参与训练冗余样本对的关键参数。但寻找最优阈值往往耗时费力，通常需要大量网格搜索。由于阈值在训练阶段无法动态调整，研究者不得不进行大量重复实验来确定阈值。为此，我们提出了一种新颖的阈值调整方法，可同时优化损失函数和样本挖掘策略中的阈值。针对样本挖掘方法，我们设计了静态非对称样本挖掘策略（ASMS）及其动态版本——自适应容忍度ASMS（AT-ASMS）。ASMS通过差异化阈值来解决单一阈值过滤样本时导致的困难（正样本对过少、冗余负样本对过多）。AT-ASMS可根据当前挖掘的正负样本对比例，在训练过程中自适应调节正负样本对的比率。这种基于元学习的阈值生成算法通过单步梯度下降获取新阈值。我们将两种阈值调整算法融合，形成双动态阈值调整策略（DDTAS）。实验结果表明，本算法在CUB200、Cars196和SOP数据集上均取得了具有竞争力的性能。