面向无监督语义哈希的位掩码鲁棒对比知识蒸馏
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing
AI总结：
针对现有知识蒸馏方法不适配语义哈希的问题，提出带位掩码机制的BRCD方法，通过对比蒸馏对齐语义空间、聚类优化鲁棒性，实现高效的无监督语义哈希模型压缩且性能优异。
摘要：
无监督语义哈希已成为快速图像检索不可或缺的技术，其目标是在不依赖标签的情况下将图像转换为二进制哈希码。该领域的最新进展表明，在无监督语义哈希模型中使用大规模骨干网络（例如ViT）可带来显著性能提升，但推理延迟已变得愈发难以忽视。知识蒸馏为缓解这一延迟提供了实用的模型压缩途径，然而主流的知识蒸馏方法并非专门为语义哈希设计，它们忽略了语义哈希独特的检索范式、蒸馏过程的内在需求以及哈希码的特性。本文提出了一种创新的位掩码鲁棒对比知识蒸馏（Bit-mask Robust Contrastive knowledge Distillation，BRCD）方法，专门用于语义哈希模型的蒸馏。为确保语义哈希场景下两种检索范式的有效性，BRCD首先通过对比知识蒸馏目标对齐教师模型与学生模型之间的语义空间；此外，为消除噪声增强并保证鲁棒优化，在蒸馏过程中引入了基于聚类的方法；进一步通过位级分析，我们发现由位独立性特性导致的冗余位的存在，为减轻其影响，在知识蒸馏目标中引入了位掩码机制。最后，大量实验不仅表明我们的BRCD方法相较于其他知识蒸馏方法具有出色的性能，还证实了该方法在不同语义哈希模型和骨干网络上的通用性。BRCD的代码可在https://github.com/hly1998/BRCD获取。
英文摘要：
Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://github.com/hly1998/BRCD.
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