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Status: Unchecked ×Keyword: nonlinear programming ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Accelerating Very Deep Convolutional Networks for Classification and Detection

   Zhang, Zou, He and Sun · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2015

   The paper presents a method to speed up test-time computation in very deep convolutional networks by accounting for nonlinear units, and reports results on VGG-16 for classification and detection.

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   1. UncheckedThe authors' method makes the VGG-16 image network run about four times faster, with top-5 error on ImageNet classification rising by only 0.3 percent.“For the widely used very deep VGG-16 model [1] , our method achieves a whole-model speedup of 4 × with merely a 0.3 percent increase of top-5 error in ImageNet classification.”
   2. UncheckedThe authors' nonlinear method uses an asymmetric reconstruction to reduce the error that builds up when ten or more layers of a CNN are approximated.“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.”
   3. UncheckedA VGG-16 network made four times faster loses accuracy only gradually when used inside the Fast R-CNN object detector.“Our 4 × accelerated VGG-16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R-CNN detector [2] .”