@article {3282, title = {Compression of Deep Neural Networks for Image Instance Retrieval}, year = {2017}, month = {01/2017}, abstract = {

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.

}, url = {https://arxiv.org/abs/1701.04923}, author = {Vijay Chandrasekhar and Jie Lin and Qianli Liao and Olivier Mor{\`e}re and Antoine Veillard and Lingyu Duan and Tomaso Poggio} } @article {3283, title = {Pruning Convolutional Neural Networks for Image Instance Retrieval}, year = {2017}, month = {07/2017}, abstract = {

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN). The objective is to heavily prune convolutional edges while maintaining retrieval performance. To this end, we introduce both data-independent and data-dependent heuristics to prune convolutional edges, and evaluate their performance across various compression rates with different deep descriptors over several benchmark datasets. Further, we present an end-to-end framework to fine-tune the pruned network, with a triplet loss function specially designed for the retrieval task. We show that the combination of heuristic pruning and fine-tuning offers 5x compression rate without considerable loss in retrieval performance.

}, keywords = {CNN, Image Instance Re- trieval, Pooling, Pruning, Triplet Loss}, url = {https://arxiv.org/abs/1707.05455}, author = {Gaurav Manek and Jie Lin and Vijay Chandrasekhar and Lingyu Duan and Sateesh Giduthuri and Xiaoli Li and Tomaso Poggio} } @article {1594, title = {Group Invariant Deep Representations for Image Instance Retrieval}, year = {2016}, month = {01/2016}, abstract = {

Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts.


In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness.


Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to over fitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-theart compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step {\textendash}quantization or hashing{\textendash} may be able to further improve the competitiveness of the descriptors.

}, author = {Olivier Mor{\`e}re and Antoine Veillard and Jie Lin and Julie Petta and Vijay Chandrasekhar and Tomaso Poggio} }