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PulseNetOne: Fast unsupervised pruning of convolutional neural networks for remote sensing
David Browne
, Michael Giering
,
Steven Prestwich
School of Computer Science and Information Technology
Insight Research Ireland Centre for Data Analytics
University College Cork
United Technologies Corporation
Research output
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peer-review
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Dive into the research topics of 'PulseNetOne: Fast unsupervised pruning of convolutional neural networks for remote sensing'. Together they form a unique fingerprint.
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Computer Science
Learning Approach
100%
Convolutional Neural Network
100%
Deep Learning Method
100%
Supervised Learning
50%
Unsupervised Learning
50%
Classification Accuracy
50%
Transfer Learning
50%
Data Augmentation
50%
Training Data
50%
Resulting Network
50%
Fully Connected Layer
50%
k-means Clustering
50%
Video Understanding
50%
Neuroscience
Neural Network
100%
Data Augmentation
50%
Biochemistry, Genetics and Molecular Biology
Remote Sensing
100%
Transfer of Learning
33%
Chemical Engineering
Transfer of Learning
50%
Transfer Learning
50%
Earth and Planetary Sciences
Data Augmentation
33%