This paper aims to accelerate the test-time computation of convolutional
neural networks (CNNs), especially very deep CNNs 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, our
method achieves a whole-model speedup of 4x with merely a 0.3% 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.

本文旨在加速卷积神经网络（CNNs）的测试时间计算，特别是对计算机视觉领域产生重大影响的非常深的 CNNs。通过开发一种不需要随机梯度下降（SGD）的有效解决方案，解决产生的非线性优化问题，我们提出了一种新的非线性方法，在对多个层进行逼近时实现了一种不对称重建，以减少快速积累误差，并成功地在 Object detection 中实现了优雅的精度降级。

加速用于分类和检测的非常深的卷积网络

Accelerating Very Deep Convolutional Networks for Classification and  Detection

This paper aims to accelerate the test-time computation of deep convolutional
neural networks (CNNs). Unlike existing methods that are designed for
approximating linear filters or linear responses, our method takes the
nonlinear units into account. We minimize the reconstruction error of the
nonlinear responses, subject to a low-rank constraint which helps to reduce the
complexity of filters. We develop an effective solution to this constrained
nonlinear optimization problem. An algorithm is also presented for reducing the
accumulated error when multiple layers are approximated. A whole-model speedup
ratio of 4x is demonstrated on a large network trained for ImageNet, while the
top-5 error rate is only increased by 0.9%. Our accelerated model has a
comparably fast speed as the "AlexNet", but is 4.7% more accurate.

本文旨在加速深度卷积神经网络的测试时间计算，通过最小化非线性响应的重建误差，附加一种低秩约束，以帮助降低过滤器的复杂度，该算法可以减小多层输入的叠加误差并提高模型精度，可将 ImageNet 的训练速度提升 4 倍，精度提高 4.7%。