Image instance retrieval is the problem of retrieving images from a database
which contain the same object. Convolutional Neural Network (CNN) based
descriptors are becoming the dominant approach for generating {\it global image
descriptors} for the instance retrieval problem. One major drawback of
CNN-based {\it global descriptors} is that uncompressed deep neural network
models require hundreds of megabytes of storage making them inconvenient to
deploy in mobile applications or in custom hardware. In this work, we study the
problem of neural network model compression focusing on the image instance
retrieval task. We study quantization, coding, pruning and weight sharing
techniques for reducing model size for the instance retrieval problem. We
provide extensive experimental results on the trade-off between retrieval
performance and model size for different types of networks on several data sets
providing the most comprehensive study on this topic. We compress models to the
order of a few MBs: two orders of magnitude smaller than the uncompressed
models while achieving negligible loss in retrieval performance.

本文主要研究如何通过量化、编码、剪枝和权值共享等技术减小深度神经网络模型的规模，以解决基于卷积神经网络的全局图像描述符在图像实例检索任务上面临的储存问题，研究结果表明，最小化模型大小能够在不影响检索结果的情况下减小两个数量级的规模。