优化三元组损失的深度距离度量学习方法

针对基于三元组损失的单一深度距离度量在多样化数据集环境下适应性差，且容易造成过拟合的问题，提出了一种优化三元组损失的深度距离度量学习方法。首先，对经过神经网络映射的三元组训练样本的相对距离进行阈值化处理，并使用线性分段函数作为相对距离的评价函数；然后，将评价函数作为一个弱分类器加入到Boosting算法中生成一个强分类器；最后，采用交替优化的方法来学习弱分类器和神经网络的参数。通过在图像检索任务中对各种深度距离度量学习方法进行评估，可以看到所提方法在CUB-200-2011、Cars-196和SOP数据集上的Recall@1值比之前最好的成绩分别提高了4.2、3.2和0.6。实验结果表明，所提方法的性能优于对比方法，同时在一定程度上避免了过拟合。

Deep distance metric learning method based on optimized triplet loss

Focused on the issues that the single deep distance metric based on triplet loss has poor adaptability to the diversified datasets and easily leads to overfitting， a deep distance metric learning method based on optimized triplet loss was proposed. Firstly， by thresholding the relative distance of triplet training samples mapped by neural network， and a piecewise linear function was used as the evaluation function of relative distance. Secondly， the evaluation function was added to the Boosting algorithm as a weak classifier to generate a strong classifier. Finally， an alternating optimization method was used to learn the parameters of the weak classifier and neural network. Through the evaluation of various deep distance metric learning methods in the image retrieval task， it can be seen that the Recall@1 of the proposed method is 4.2， 3.2 and 0.6 higher than that of the previous best score on CUB-200-2011， Cars-196 and SOP datasets respectively. Experimental results show that the proposed method outperforms the comparison methods， while avoiding overfitting to a certain extent.