A Perspective of the Noise Removal for Faster Neural Network Training

A Perspective of the Noise Removal for Faster Neural Network Training

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Image classification is widely used within image processing area. It is known that insufficient amount of data has negative impact on the training of neural networks in terms of accuracy, convergence speed and in some cases even in the inability to converge. On the other hand, big amount of data significantly increases the training time and costs needed for model creation. Every training sample contains the part valuable for decision (face in case of this paper) and noise, i.e. background of the object. This paper introduces method of iterative noise removal during the training with combination with the transfer learning to optimize the speed of the training process. We show the combination of proposed noise removal and transfer learning leads to more effective training process and enables to learn also from limited data sets. The main contribution of this paper is a proposed method that reduces training time and it is able to accelerate the process in average by 69%. The method was tested on binary classification of two persons from LFW database.

Image classification is widely used within image processing area. It is known that insufficient amount of data has negative impact on the training of neural networks in terms of accuracy, convergence speed and in some cases even in the inability to converge. On the other hand, big amount of data significantly increases the training time and costs needed for model creation. Every training sample contains the part valuable for decision (face in case of this paper) and noise, i.e. background of the object. This paper introduces method of iterative noise removal during the training with combination with the transfer learning to optimize the speed of the training process. We show the combination of proposed noise removal and transfer learning leads to more effective training process and enables to learn also from limited data sets. The main contribution of this paper is a proposed method that reduces training time and it is able to accelerate the process in average by 69%. The method was tested on binary classification of two persons from LFW database.

machine learning; image classification; transfer learning; limited data set; noise removal; optimization

machine learning; image classification; transfer learning; limited data set; noise removal; optimization

RAJNOHA, M.; MIKULEC, V.; BURGET, R.; DRAŽIL, J.

2020

28.10.2019

Dublin

978-1-7281-5763-4

2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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https://ieeexplore.ieee.org/document/8970907