You need an account to view media

Sign in to view media

Don't have an account? Please contact us to request an account.

IJCNN Regular Sessions
Oral
Deep neural networks

Efficient Search for the Number of Channels for Convolutional Neural Networks

Hui Zhu

Date & Time

Mon, July 20, 2020

5:45 pm – 7:45 pm

Location

On-Demand

Abstract

Latest algorithms for automatic neural architecture search perform remarkably but few of them can effectively design the number of channels for convolutional neural networks and consume less computational efforts. In this paper, we propose a method for efficient automatic search which is special to the widths of networks instead of the connections within neural architectures. Our method, functionally incremental search based on function-preserving, will explore the number of channels for almost any convolutional neural network rapidly while controlling the number of parameters and even the amount of computations (FLOPs). On CIFAR-10 and CIFAR-100 classification, our method using minimal computational resources (0.41-1.29 GPU-days) can discover more effective rules of the widths of networks to improve the accuracy (a-1.08 on CIFAR-10 and b-2.33 on CIFAR-100) with fewer number of parameters.


Presenter

Hui Zhu

Institute of Computing Technology, Chinese Academy of Sciences
Sign in to join the conversationDon't have an account? Please contact us to request an account.
Sign in to view documentsDon't have an account? Please contact us to request an account.

Session Chairs

Baozhou Zhu

Delft University of Technology

Zaid Al-Ars

Delft University of Technology