Learning the non-linearity in convolutional neural networks
We propose the introduction of nonlinear operation into the feature generation process in
convolutional neural networks. This nonlinearity can be implemented in various ways. First
we discuss the use of nonlinearities in the process of data augmentation to increase the
robustness of the neural networks recognition capacity. To this end, we randomly disturb the
input data set by applying exponents within a certain numerical range to individual data
points of the input space. Second we propose nonlinear convolutional neural networks …
convolutional neural networks. This nonlinearity can be implemented in various ways. First
we discuss the use of nonlinearities in the process of data augmentation to increase the
robustness of the neural networks recognition capacity. To this end, we randomly disturb the
input data set by applying exponents within a certain numerical range to individual data
points of the input space. Second we propose nonlinear convolutional neural networks …
Learning the non-linearity in convolutional neural networks
G Singh Chadha, A Schwung - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
We propose the introduction of nonlinear operation into the feature generation process in
convolutional neural networks. This nonlinearity can be implemented in various ways. First
we discuss the use of nonlinearities in the process of data augmentation to increase the
robustness of the neural networks recognition capacity. To this end, we randomly disturb the
input data set by applying exponents within a certain numerical range to individual data
points of the input space. Second we propose nonlinear convolutional neural networks …
convolutional neural networks. This nonlinearity can be implemented in various ways. First
we discuss the use of nonlinearities in the process of data augmentation to increase the
robustness of the neural networks recognition capacity. To this end, we randomly disturb the
input data set by applying exponents within a certain numerical range to individual data
points of the input space. Second we propose nonlinear convolutional neural networks …
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