Dynamic-stride-net: deep convolutional neural network with dynamic stride

By Zerui Yang, Yuhui Xu, Wenrui Dai, H. Xiong. 2019

9 citations recorded by the source corpus.

It is crucial to reduce the cost of deep convolutional neural networks while preserving their accuracy. Existing methods adaptively prune DNNs in a layer-wise or channel-wise manner based on the input image. In this paper, we develop a novel dynamic network, namely Dynamic-Stride-Net, to improve residual network with layer-wise adaptive strides in the convolution operations. Dynamic-Stride-Net leverages a gating network to adaptively select the strides of convolutional blocks based on the outputs of the previous layer. To optimize the selection of strides, the gating network is trained by reinforcement learning. The floating point operations per second (FLOPS) is significantly reduced by adapting the strides to convolutional layers without loss of accuracy. Dynamic-Stride-Net reduces the computational cost by 35%-50% with equivalent accuracy of the original model on CIFAR-10 and CIFAR-100 datasets. It outperforms the state-of-the-art dynamic networks and static compression methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang
  7. Quoc V. Le
  8. Alan L. Yuille

In Computer Vision and Pattern Recognition

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Yizeng Han
  5. Le Yang
  6. Honghui Wang
  7. Luc Van Gool
  8. Alan L. Yuille

In Model Architectures

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Honghui Wang
  5. Le Yang
  6. Yizeng Han
  7. Quoc V. Le
  8. Alan L. Yuille

Citations in the corpus