  Existing data-dependent hashing methods use large backbone networks with
millions of parameters and are computationally complex. Existing knowledge
distillation methods use logits and other features of the deep (teacher) model
and as knowledge for the compact (student) model, which requires the teacher's
network to be fine-tuned on the context in parallel with the student model on
the context. Training teacher on the target context requires more time and
computational resources. In this paper, we propose context unaware knowledge
distillation that uses the knowledge of the teacher model without fine-tuning
it on the target context. We also propose a new efficient student model
architecture for knowledge distillation. The proposed approach follows a
two-step process. The first step involves pre-training the student model with
the help of context unaware knowledge distillation from the teacher model. The
second step involves fine-tuning the student model on the context of image
retrieval. In order to show the efficacy of the proposed approach, we compare
the retrieval results, no. of parameters and no. of operations of the student
models with the teacher models under different retrieval frameworks, including
deep cauchy hashing (DCH) and central similarity quantization (CSQ). The
experimental results confirm that the proposed approach provides a promising
trade-off between the retrieval results and efficiency. The code used in this
paper is released publicly at \url{https://github.com/satoru2001/CUKDFIR}.
