On the Reduction of Computational Complexity of Deep Convolutional Neural Networks
Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets i...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-04-01
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Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/20/4/305 |