2019/06/20 by Le Thanh Nguyen-Meidine, Nguyen-Meidine, Le Thanh, Éric Granger +7 · 1 citation
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image Enhancement Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1906.08746
openalex publication_date 2019/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in\nmany visual recognition tasks,the growing computational complexity and energy\ncon-sumption of networks remains an issue, especially for ap-plications on\nplatforms with limited resources and requir-ing real-time processing. Filter\npruning techniques haverecently shown promising results for the compression\nandacceleration of convolutional NNs (CNNs). However, thesetechniques involve\nnumerous steps and complex optimisa-tions because some only prune after\ntraining CNNs, whileothers prune from scratch during training by\nintegratingsparsity constraints or modifying the loss function.In this paper we\npropose a new Progressive GradientPruning (PGP) technique for iterative filter\npruning dur-ing training. In contrast to previous progressive\npruningtechniques, it relies on a novel filter selection criterion thatmeasures\nthe change in filter weights, uses a new hard andsoft pruning strategy and\neffectively adapts momentum ten-sors during the backward propagation pass.\nExperimentalresults obtained after training various CNNs on image datafor\nclassification, object detection and domain adaptationbenchmarks indicate that\nthe PGP technique can achievea better trade-off between classification accuracy\nand net-work (time and memory) complexity than PSFP and otherstate-of-the-art\nfilter pruning techniques.\n