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Frames Learned by Prime Convolution Layers in a Deep Learning Framework
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DOI：10.1109/TNNLS.2020.3009059.png)
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摘要
En 中文
This brief addresses understandability of modern machine learning networks with respect to the statistical properties of their convolution layers. It proposes a set of tools for categorizing a convolution layer in terms of kernel property (meanlet, differencelet, or distrotlet) or kernel sequence property (frame spectra and intralayer correlation matrix). These tools are expected to be relevant for determining the generalization capabilities of a convolutional neural network. In particular, this brief highlights that the less frequency penalizing network among AlexNet, GoogleNet, RESNET101, and VGG19 is the more relevant one in terms of solutions for low-level ice-sheet feature enhancement.
Keyword:
Kernel
Convolution
Standards
Training
Harmonic analysis
Machine learning
Learning systems
Convolution frames
convolutional neural network (CNN)
deep learning
differencelet
distrotlet
meanlet
wavelet
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