Flattening Layer Pruning in Convolutional Neural Networks
نویسندگان
چکیده
The rapid growth of performance in the field neural networks has also increased their sizes. Pruning methods are getting more and attention order to overcome problem non-impactful parameters overgrowth neurons. In this article, application Global Sensitivity Analysis (GSA) demonstrates impact input variables on model’s output variables. GSA gives ability mark out least meaningful arguments build reduction algorithms these. Using several popular datasets, study shows how different levels pruning correlate network accuracy negligibly accuracy. doing so, pre- post-reduction sizes compared. This paper Sobol FAST with common norms can largely decrease size a network, while keeping relatively high. On basis obtained results, it is possible create thesis about asymmetry between elements removed from topology quality network.
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ژورنال
عنوان ژورنال: Symmetry
سال: 2021
ISSN: ['0865-4824', '2226-1877']
DOI: https://doi.org/10.3390/sym13071147