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• By reducing the spatial dimensions, pooling helps lower the risk of overfitting by simplifying the model,
ensuring it generalises well to new, unseen data.
Max
Pooling
Sum
A small difference in the input image will create a very similar pooled image.
Fully Connected Layer
This is the last and the final layer of the Convolutional Neural Network. After the features of the input image
are extracted by the convolution layers and downsampled by the pooling layers, their output is a 3-dimensional
matrix which is flattened into a vector of values. These values of the single vector represent a specific feature of a
specific label and are redirected to fully connected layers to predict the final outputs of the network. This helps in
classifying an image into a specific label based on the probability of the input being in a specific class. For example,
the input image with a beak belongs to the category of a bird.
bird p bird
sunset p sunset
dog p dog
... p
cat cat
Convolution + Nonlinearity Max pooling Vec ...
Convolution + Pooling layers Fully connected layers Nx binary classification
Image Classification Using CNN
Brainy Fact
The first image recognition CNN-AlexNet named after(creator-Alex Krizhevsky), won the 2012 ImageNet
Computer Vision contest with 85% accuracy.
Computer Vision (Practical) 355

