In image analysis, #convolutional neural networks (#CNNs or #ConvNets for short) are time and memory efficient than fully connected (#FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is #ConvNet derived from FC networks? Where the term #convolution in CNNs came from? These questions are to be answered in this #presentation.
Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.
Derivation of Convolutional Neural Network from Fully Connected Network Step-by-Step
1. Derivation of Convolutional Neural
Network from Fully Connected
Network Step-by-Step
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
MENOUFIA UNIVERSITY
FACULTY OF COMPUTERS AND INFORMATION
المنوفية جامعة
الحاسبات كليةوالمعلومات
المنوفية جامعة
Ahmed F. Gad 18-May-2018
2. 15 8 9
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Input Image
3x3
Ahmed F. Gad
42. Hidden Layer 1
500 Neuron
Input Layer
1,024 Neuron
Input Image
32x32 Too Many Parameters
Ahmed F. Gad
43. 1,024*500
Hidden Layer 1
500 Neuron
Input Layer
1,024 Neuron
512,000=
Input Image
32x32 Too Many Parameters
Ahmed F. Gad
44. 1,024*500
Hidden Layer 1
500 Neuron
Input Layer
1,024 Neuron
512,000=
Input Image
32x32
CNN can create a large network but with less number of
parameters than FC networks
Too Many Parameters
Ahmed F. Gad
81. 15 8 9
10 17 22
20 3015
15
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10
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15
Input Image
3x3
Vector 9x1 0
1
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Hidden Layer
4 Groups
4 Filters
4 Feature Maps
Ahmed F. Gad
4 Neurons
Each neuron will process an
image region of a specific size.
In this example, region size is
2x2.