The document discusses convolutional neural networks and their use of convolutional layers and rectifiers. It provides diagrams showing how convolutional layers extract feature maps from an input image using filters. Rectifiers are then used to introduce nonlinearity. References are provided for further reading on convolutional neural networks and analyzing the impact of different rectifier activation functions.

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Input Image
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We create many
feature maps to
obtain our first
convolution layer
Convolutional Layer
Feature Maps

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Input Image
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0 0 0 0 0 0 0
Convolutional Layer
Feature Maps
0
y
Rectifier

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Image Source: http://mlss.tuebingen.mpg.de/2015/slides/fergus/Fergus_1.pdf

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Image Source: http://mlss.tuebingen.mpg.de/2015/slides/fergus/Fergus_1.pdf

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Image Source: http://mlss.tuebingen.mpg.de/2015/slides/fergus/Fergus_1.pdf

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Understanding Convolutional
Neural Networks with A
Mathematical Model
By C.-C. Jay Kuo (2016)
Link:
https://arxiv.org/pdf/1609.04112.pdf
Additional Reading:

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Delving Deep into Rectifiers:
Surpassing Human-Level
Performance on ImageNet
Classification
By Kaiming He et al. (2015)
Link:
https://arxiv.org/pdf/1502.01852.pdf
Additional Reading: