NDLI: Accelerating convolutional neural network training using ProMoD backpropagation algorithm

Content Provider	IET Digital Library
Author	Gürhanlı, Ahmet
Abstract	Convolutional neural networks (CNNs) play an important role in image recognition applications. Fast training of image recognition systems is a crucial point, because the system should be trained for each new image class. These networks are trained using lengthy calculations. Focus of engineering is on obtaining a fast, but stable optimisation method. Momentum technique which is used in backpropagation algorithms is like a proportional–integral (PI) controller that is widely employed in automatic control systems. It takes the integral of past errors and helps reaching the training targets. Proportional + momentum + derivative (ProMoD) method adds gradient of update matrices to the training process and builds an optimiser such as the widely used PI–derivative controller. The method accelerates the movement toward the target accuracy levels. This is achieved by doing bigger corrections in the beginning using the differences in the calculated update matrices. In this research, ProMoD method is tested on image recognition applications and CNNs. Modified national institute of standards and technology database (MNIST) and Fashion-MNIST datasets are used for evaluating the performance. Experimental results showed that ProMoD might perform much faster in training of CNNs and consume proportionally less power with respect to the momentum and stochastic gradient descent (SGD) techniques.
Starting Page	2957
Ending Page	2964
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0761
Journal	IET Image Processing
Publisher Date	2020-03-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Control System Backpropagation Backpropagation Algorithm Calculated Update Matrices CNN Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Convolutional Neural Network Training Crucial Point Fast Training Gradient Method Image Class Image Recognition Image Recognition Application Image Recognition System Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Lengthy Calculations Momentum Technique Neural Computing Technique Numerical Analysis PI–derivative Controller ProMoD Backpropagation Algorithm ProMoD Method Proportional + Momentum + Derivative Method Proportional-integral Controller Stable Optimisation Method Target Accuracy Levels Training Process Training Targets
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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