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31 January 2020 Training the convolutional neural network with statistical dependence of the response on the input data distortion
Igor M. Janiszewski, Vladimir V. Arlazarov, Dmitry G. Slugin
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330W (2020) https://doi.org/10.1117/12.2559457
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
The paper proposes an approach to training a convolutional neural network using information on the level of distortion of input data. The learning process is modified with an additional layer, which is subsequently deleted, so the architecture of the original network does not change. As an example, the LeNet5 architecture network with training data based on the MNIST symbols and a distortion model as Gaussian blur with a variable level of distortion is considered. This approach does not have quality loss of the network and has a significant error-free zone in responses on the test data which is absent in the traditional approach to training. The responses are statistically dependent on the level of input image’s distortions and there is a presence of a strong relationship between them.
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Igor M. Janiszewski, Vladimir V. Arlazarov, and Dmitry G. Slugin "Training the convolutional neural network with statistical dependence of the response on the input data distortion", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330W (31 January 2020); https://doi.org/10.1117/12.2559457
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KEYWORDS
Distortion
Data modeling
Network architectures
Convolutional neural networks
Neural networks
Optical character recognition
Video
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