# Efficient and Accurate Approximations of Nonlinear Convolutional Networks

Source: [Efficient and Accurate Approximations of Nonlinear Convolutional Networks](<https://www.sota2.com/research/paper/68fa61961e209131ce9f4d8c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:01:28.850Z

Authors: Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, Jian Sun

Published: 2014-11-16T08:37:25.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1411.4229>)

[PDF](<https://arxiv.org/pdf/1411.4229.pdf>)