请按照顶级期刊的表达方式将以下内容翻译成英文：2设计基于对比学习和生成对抗网络的图像哈希检索算法 针对传统视觉神经网络存在鲁棒性较差、参数过多等问题本文利用对比学习和生成对抗网络等技术设计一种轻量级的鲁棒图像哈希检索算法。该算法通过自监督对抗训练获得鲁棒教师网络然后利用生成对抗网络对学生网络进行训练提高网络模型的鲁棒性接着模仿免疫注射进行知识蒸馏在保证模型性能的同时有效压缩网络最后利用基于卷积模块

Designing an Image Hash Retrieval Algorithm based on Contrastive Learning and Generative Adversarial Networks

To address the issues of poor robustness and excessive parameters of traditional visual neural networks, this paper proposes a lightweight and robust image hash retrieval algorithm utilizing contrastive learning and generative adversarial networks. By employing self-supervised adversarial training, the algorithm obtains a robust teacher network, and then trains the student network using generative adversarial networks to enhance the network model's robustness. Next, the algorithm imitates immune injection to distill knowledge while effectively compressing the network, ensuring model performance. Finally, an attention mechanism based on convolution modules is utilized to extract the image hash sequence. Experimental results on public datasets indicate that the proposed image hash retrieval algorithm outperforms various benchmark hash retrieval algorithms, with better robustness and fewer model parameters.