APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Through convolutional equivariance
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Through convolutional equivariance and approximate translation invariance with pooling
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Through convolutional equivariance and exact pooling invariance
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They exist in a higher dimensional invariant space The convolutional layers are shift equivariant. If an input image is shifted a little bit, the convolutional filters will produce the same response at the shifted location. The pooling layers are approximately shift invariant. For example, if an input image is shifted a little bit under a max pooling layer, the maximum value will still be the same. Overall, given an input image x, a shift S, a shift equivariant convolutional layer f, and a shift invariant pooling layer g, the ConvNet g(f(x)) is shift invariant because g(f(Sx)) = g(Sf(x)) = g(f(x)). (see Note02)
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Detailed explanation-1: -Shift-invariance: this means that if we shift the input in time (or shift the entries in a vector) then the output is shifted by the same amount.
Detailed explanation-2: -It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling operations they are endowed with. In fact, several works have found that these networks systematically fail to recognise new objects on untrained locations.
Detailed explanation-3: -The equivariance allows the network to generalise edge, texture, shape detection in different locations. The invariance allows precise location of the detected features to matter less. These are two complementary types of generalisation for many image processing tasks.