Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve the best results, they use millions of parameters to be trained. However, when targetting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers.We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods.
CITATION STYLE
Soulié, G., Gripon, V., & Robert, M. (2016). Compression of deep neural networks on the fly. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9887 LNCS, pp. 153–160). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_19
Mendeley helps you to discover research relevant for your work.