2-D convolution. Given the input and two kernels as shown in Fig. 10.43, a. compute the feature...

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2-D convolution. Given the input and two kernels as shown in Fig. 10.43,

a. compute the feature maps by applying kernel K1,K2 (stride is defined as 1 , and zeros are padded around I ), respectively;

b. re-compute the feature maps if the stride is 2 ;

c. compute the new feature maps after we apply a downsampling process, max pooling, and average pooling (filter size is 2×2 ), respectively, to the obtained feature maps in (a).

FIGURE 10.43 0 0 1 101010 1101001 0010010 1 0 1 0 1 1 1 Input / 1 001 100 10 11 0 1 1 0 0 1 0 1 0 1 0 1 0 1 0

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Data Mining Concepts And Techniques

ISBN: 9780128117613

4th Edition

Authors: Jiawei Han, Jian Pei, Hanghang Tong

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