请按照顶级期刊的表达方式将以下内容翻译成英文：3提出基于知识蒸馏和最优传输技术的人脸哈希检索算法 针对视觉神经网络模型参数过多、计算复杂度高和细粒度检索不准确等问题本文利用知识蒸馏和最优传输技术提出一种面向人脸图像的轻量化哈希检索算法。该算法的一个重要贡献是设计出基于注意力机制的三元组知识蒸馏其中损失函数由注意力损失、Kullback-Leibler损失和身份损失三部分组成。该知识蒸馏方案可以关注

Proposing a Facial Hash Retrieval Algorithm Based on Knowledge Distillation and Optimal Transport Technology

In response to the problems of excessive model parameters, high computational complexity, and inaccurate fine-grained retrieval of visual neural networks, this article proposes a lightweight facial hash retrieval algorithm based on knowledge distillation and optimal transport technology. One important contribution of this algorithm is the design of a triplet knowledge distillation based on attention mechanism, where the loss function consists of attention loss, Kullback-Leibler loss, and identity loss. This knowledge distillation scheme can focus on the salient regions of the face and reduce network parameters. Another contribution is the design of a hash quantization scheme based on optimal transport technology. This scheme partitions the facial feature space by calculating class centers and uses optimal transport technology to achieve binary quantization, effectively improving hash retrieval performance. In addition, an alternating training strategy is designed to fine-tune the parameters of the lightweight hash network. Experimental results show that the lightweight hash retrieval algorithm performs better than some famous hash retrieval algorithms on two benchmark facial datasets.