Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 38, Issue 10, Pages 1943-1955Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2015.2502579
Keywords
Convolutional neural networks; acceleration; image classification; object detection
Ask authors/readers for more resources
This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs[1] that have substantially impacted the computer vision community. Unlike previous methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We develop an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD). More importantly, while previous methods mainly focus on optimizing one or two layers, our nonlinear method enables an asymmetric reconstruction that reduces the rapidly accumulated error when multiple (e.g., >= 10) layers are approximated. For the widely used very deep VGG-16 model[1], our method achieves a whole-model speedup of 4x with merely a 0.3 percent increase of top-5 error in ImageNet classification. Our 4x accelerated VGG-16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R-CNN detector[2].
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available