Convolution Neural Network

What is the primary purpose of the convolution operation in image processing?

• To reduce the size of an image

• To extract features from an image using a defined kernel matrix

• To increase the color intensity of an image

• To convert an image from grayscale to color

What is the primary purpose of adding padding in a convolution operation?

• To increase the color intensity of the image edges

• To shrink the original image size during convolution

• To prevent the image from overlapping during convolution

• To preserve the size of the original image and ensure edge features are utilized

What does the term "stride" refer to in the context of convolution operations in image processing?

• The size of the kernel used for the convolution

• The number of pixels the kernel shifts over the input matrix

• The amount of padding added to the image

• The resolution of the input image

What is the primary function of a pooling layer in a Convolutional Neural Network (CNN)?

• To increase the resolution of the image representation

• To progressively reduce the spatial size of the representation, reducing network complexity and computational cost

• To apply convolutional operations with larger kernel sizes

• To add padding to the input image

What is the primary purpose of Max Pooling in a Convolutional Neural Network (CNN)?

• To take the average of a region in the image

• To select the maximum value from a region, highlighting the most important features

• To increase the resolution of the image

• To darken the background of the image

What is the primary difference between Average Pooling and Max Pooling in a Convolutional Neural Network (CNN)?

• selects the minimum value from a region, while Max Pooling selects the maximum value.

• Average Pooling blends all values in a region, while Max Pooling only retains the maximum value.

• Average Pooling increases the resolution, while Max Pooling decreases it.

• Average Pooling is used for color images, while Max Pooling is used for grayscale images.

What is the primary purpose of Convolutional Neural Networks (CNNs) in deep learning?

• To perform natural language processing tasks.

• To process grid-like data such as images and video frames.

• To model sequential data like time series and speech.

• To improve the efficiency of search algorithms.

What is the primary application of the LeNet-5 architecture?

• Object detection in real-time video.

• Speech recognition.

• Handwritten digit recognition.

• Natural language processing.

Which of the following statements about AlexNet is TRUE?

• introduced by Yann LeCun and has 50 million parameters with 7 layers.

• first CNN architecture to use ReLUs as activation functions and won the 2012 ImageNet ILSVRC challenge with a top-5 error rate of 17%

• introduced by Geoffrey Hinton, has 80 million parameters, and uses sigmoid activation functions.

• the first CNN to use CPUs for training, achieving a top-5 error rate of 26% in the 2012 ImageNet ILSVRC challenge.

Which of the following statements about GoogLeNet is TRUE?

• created by Yann LeCun and has approximately 60 million parameters, achieving a top-5 error rate of around 17% in the ILSVRC 2014 challenge.

• developed by Christian Szegedy from Google Research, has roughly 6 million parameters and achieved a top-5 error rate of below 7% in the ILSVRC 2014 challenge.

• introduced by Geoffrey Hinton, has more parameters than AlexNet, and uses ReLU activation functions to achieve a top-5 error rate of around 10%.

• uses inception modules to reduce the depth of the network and was the first CNN to win the ILSVRC challenge with a top-5 error rate of 26%.