Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling

Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs are complex models that require the use of powerful hardware, both for training and deploying them. To this end, a quantization-based poolin...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 30; H. 6; S. 1705 - 1715
Hauptverfasser:	Passalis, Nikolaos, Tefas, Anastasios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Backpropagation algorithms Bag-of-features (BoF) Basis functions Computational modeling Computer architecture Computer vision convolutional neural networks (CNNs) Feature extraction Image classification Learning Lightweight lightweight neural networks Measurement Neural networks pooling operators Quantization (signal) Radial basis function Task analysis Task complexity Training
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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