  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.
