CNN vs Fully Connected Network for Image Processing

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Questions and Answers

What is the purpose of the activation function in a convolutional neural network?

To reduce the dimensions of the feature maps

To perform convolution operations

To introduce non-linearity into the network (correct)

To flatten the feature maps

What is the main purpose of the pooling layer in a convolutional neural network?

To extract features from the input data

To perform convolution operations

To reduce the dimensions of the feature maps (correct)

To introduce non-linearity into the network

What is the purpose of the flatten layer in a convolutional neural network?

To introduce non-linearity into the network

To enable the network to process the extracted features (correct)

To perform convolution operations

To reduce the dimensions of the feature maps

What is the output layer responsible for in a convolutional neural network?

Producing classifications for the input data Signup and view all the answers

Why is it not preferred to use a large number of fully connected layers?

Because it increases the complexity of the network Signup and view all the answers

What is the purpose of the convolutional layer in a convolutional neural network?

To extract features from the input data Signup and view all the answers

What is the advantage of CNNs over FC networks when dealing with images?

CNNs preserve the dimensional relationships between image pixels, while FC networks do not Signup and view all the answers

What is the purpose of the convolutional layer in a CNN?

To extract relevant features from the input data Signup and view all the answers

What is the purpose of max pooling in a convolutional neural network?

To reduce the dimensions of the feature maps Signup and view all the answers

What is the purpose of the hidden layers in a convolutional neural network?

To find mathematical relationships between the extracted features Signup and view all the answers

How many filters are typically applied to the input data in a convolutional layer?

A specified number of filters, each searching for one feature in the image Signup and view all the answers

What is the advantage of CNNs in terms of the number of trainable parameters?

CNNs have a smaller number of parameters to train, resulting in faster training Signup and view all the answers

What is the role of the input layer in a CNN?

To receive the raw pixel values of the input image and pass them forward to subsequent layers Signup and view all the answers

What can the input layer in a CNN accept?

Images of any size and format Signup and view all the answers

What is the number of trainable parameters in a filter with size 3x3 and 4 channels?

36 Signup and view all the answers

What is the purpose of a filter in a convolutional layer?

To extract one feature from the input image Signup and view all the answers

What is the resulting image after applying a filter to an input image?

Feature map Signup and view all the answers

What is the mathematical equation of a filter in a convolutional layer?

Y = w * X + b Signup and view all the answers

Why do we need multiple filters in a convolutional layer?

To detect multiple features from the input image Signup and view all the answers

What is the relationship between the number of channels in the input image and the number of channels in the filter?

The number of channels in the filter should be equal to the number of channels in the input image Signup and view all the answers

What is the primary function of a convolutional layer?

To extract features from the input image Signup and view all the answers

What is the characteristic of the convolution operation in a convolutional layer?

Linear Signup and view all the answers

Study Notes

CNN vs. Fully-Connected Network

CNNs are better than FCNs when dealing with images because they preserve the dimensional relationships between image pixels, whereas FCNs rearrange them, resulting in the loss of this dimensional relationship.
CNNs have a smaller number of parameters to train, which means faster and more accurate training.

CNN Construction

The input layer receives raw pixel values of the input image and passes them forward to subsequent layers for feature extraction and classification.
Convolutional layers apply specified filters to the input data to extract relevant features, with each filter searching for one feature in the image.
Activation functions are applied after convolution to add non-linearity to the network.
Pooling layers (max or avg) reduce the dimensions of feature maps obtained from convolutional layers, extracting the most important information.
Flatten layers serve as a bridge between convolutional and fully connected layers, enabling the network to process extracted features and make predictions.

Convolutional Layers

Each filter extracts one feature from the image, and the network determines the feature during training.
Each filter produces one feature map, and applying multiple filters detects multiple features from the image.
The number of trainable parameters in a layer is calculated by counting the number of filter parameters.

Convolutional Operation

The mathematical equation of a filter is Y = w * X + b, making the convolution operation a linear function.

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Description

Compare and contrast Convolutional Neural Networks (CNNs) and Fully Connected (FC) Networks for image processing, including their advantages and disadvantages.