  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.
