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Introduction to Convolutional Neural Networks

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SOLVED: 20 points) Consider a convolution layer. The input consists of 4 feature maps of sizeconvolution is done with a stride of 2 and no zero padding(a) (4 points) What is the size of the resulting feature map?(b) (4 points) Determine the number of parameters in this convolution layer. (Biases are used)(c) (4 points) Now suppose we made this a fully connected layer, but where the number of input and output units are kept the same as in the network described above. Determine the number of weigh

SOLVED: 20 points) Consider a convolution layer. The input consists of 4 feature maps of sizeconvolution is done with a stride of 2 and no zero padding(a) (4 points) What is the size of the resulting feature map?(b) (4 points) Determine the number of parameters in this convolution layer. (Biases are used)(c) (4 points) Now suppose we made this a fully connected layer, but where the number of input and output units are kept the same as in the network described above. Determine the number of weigh

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U-Net is a powerful neural network architecture often used in diffusion models for image generation and restoration tasks. Initially designed for biomedical image segmentation, U-Net has found broader applications due to its ability to effectively capture high-level and low-level features. It employs a symmetric encoder-decoder structure with skip connections that bridge the corresponding encoder and decoder layers. This design allows the network to maintain spatial information lost during down-

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