Improving Deep Neural Network Performance with Kernelized Min-Max Objective

2018 ◽  
pp. 182-191
Author(s):  
Kai Yao ◽  
Kaizhu Huang ◽  
Rui Zhang ◽  
Amir Hussain
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Deep Neural Network

scholarly journals Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 73-84
Author(s):  
Md. Shahadat Hossain ◽  
Md. Anwar Hossain ◽  
AFM Zainul Abadin ◽  
Md. Manik Ahmed
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Performance ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Digit Recognition ◽  
Optical Character ◽  
Regularization Parameters ◽  
Handwritten Recognition

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

scholarly journals Transfer Learning Using Rotated Image Data to Improve Deep Neural Network Performance

2014 ◽  
pp. 290-300 ◽  
Author(s):  
Telmo Amaral ◽  
Luís M. Silva ◽  
Luís A. Alexandre ◽  
Chetak Kandaswamy ◽  
Joaquim Marques de Sá ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Performance ◽  
Deep Neural Network ◽  
Image Data

scholarly journals Simulating Time-Series Data for Improved Deep Neural Network Performance

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 131248-131255 ◽  
Author(s):  
Jordan Yeomans ◽  
Simon Thwaites ◽  
William S. P. Robertson ◽  
David Booth ◽  
Brian Ng ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Performance ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Series Data

Improving deep neural network performance by integrating kernelized Min-Max objective

2020 ◽  
Vol 408 ◽  
pp. 82-90 ◽  
Author(s):  
Qiu-Feng Wang ◽  
Kai Yao ◽  
Rui Zhang ◽  
Amir Hussain ◽  
Kaizhu Huang
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Deep Neural Network

scholarly journals An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction Data

2019 ◽  
Vol 157 ◽  
pp. 25-30 ◽  
Author(s):  
Favorisen Rosyking Lumbanraja ◽  
Bharuno Mahesworo ◽  
Tjeng Wawan Cenggoro ◽  
Arif Budiarto ◽  
Bens Pardamean
Keyword(s):  
Neural Network ◽  
Protein Phosphorylation ◽  
Network Performance ◽  
Deep Neural Network ◽  
Phosphorylation Site ◽  
Site Prediction ◽  
Limited Protein

scholarly journals Improving Deep Neural Network Performance by Reusing Features Trained with Transductive Transference

2014 ◽  
pp. 265-272 ◽  
Author(s):  
Chetak Kandaswamy ◽  
Luís M. Silva ◽  
Luís A. Alexandre ◽  
Jorge M. Santos ◽  
Joaquim Marques de Sá
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Deep Neural Network

scholarly journals Book spine recognition with the use of deep neural networks

2020 ◽  
Vol 44 (6) ◽  
pp. 968-977
Author(s):  
M.O. Kalinina ◽  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Performance ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Original Data ◽  
Network Architectures ◽  
Data Set ◽  
The Neural Network ◽  
Processing Information

Nowadays deep neural networks play a significant part in various fields of human activity. Especially they benefit spheres dealing with large amounts of data and lengthy operations on obtaining and processing information from the visual environment. This article deals with the development of a convolutional neural network based on the YOLO architecture, intended for real-time book recognition. The creation of an original data set and the training of the deep neural network are described. The structure of the neural network obtained is presented and the most frequently used metrics for estimating the quality of the network performance are considered. A brief review of the existing types of neural network architectures is also made. YOLO architecture possesses a number of advantages that allow it to successfully compete with other models and make it the most suitable variant for creating an object detection network since it enables some of the common disadvantages of such networks to be significantly mitigated (such as recognition of similarly looking, same-color book coves or slanted books). The results obtained in the course of training the deep neural network allow us to use it as a basis for the development of the software for book spine recognition.

scholarly journals GraphChallenge.org Sparse Deep Neural Network Performance

2020 ◽  
Author(s):  
Jeremy Kepner ◽  
Simon Alford ◽  
Vijay Gadepally ◽  
Michael Jones ◽  
Lauren Milechin ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Deep Neural Network
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