Uncertainty Propagation in Convolutional Neural Networks: Technical Report
@article{Tzelepis2021UncertaintyPI, title={Uncertainty Propagation in Convolutional Neural Networks: Technical Report}, author={Christos Tzelepis and I. Patras}, journal={ArXiv}, year={2021}, volume={abs/2102.06064} }
In this technical report we study the problem of propagation of uncertainty (in terms of variances of given univariate normal random variables) through typical building blocks of a Convolutional Neural Network (CNN). These include layers that perform linear operations, such as 2D convolutions, fullyconnected, and average pooling layers, as well as layers that act non-linearly on their input, such as the Rectified Linear Unit (ReLU). Finally, we discuss the sigmoid function, for which we give…
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