Accelerating Very Deep Convolutional Networks for Classification and Detection
This paper aims to accelerate the test time computation of convolutional neural networks (CNNs) especially very deep CNNs <xref ref type="bibr" rid="ref1"[1]/xref> 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. <inline formula> <tex math notation="LaTeX"\ge$/tex math><alternatives><inline graphic xlink:type="simple" xlink:href="he ieq1 2502579.gif" /alternatives>/inline formula>10) layers are approximated. For the widely used very deep VGG 16 model <xref ref type="bibr" rid="ref1"[1]/xref> our method achieves a whole model speedup of 4<inline formula><tex math notation="LaTeX"\times$/tex math> <alternatives><inline graphic xlink:type="simple" xlink:href="he ieq2 2502579.gif"/alternatives>/inline formula> with merely a 0.3 percent increase of top 5 error in ImageNet classification. Our 4<inline formula><tex math notation="LaTeX"\times$ /tex math><alternatives><inline graphic xlink:type="simple" xlink:href="he ieq3 2502579.gif"/alternatives>/inline formula> accelerated VGG 16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R CNN detector <xref ref type="bibr" rid="ref2"[2]/xref>
IEEE Transactions on Pattern Analysis and Machine Intelligence