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How to get Complexity per Layer, Sequential Operations and Maximum Path Length in CNN architecture?

They don't seem to be sharing any supplementary material with details on this, however as they state, convolution being independent from the input size can be applied to varying size input (see ...
Alberto's user avatar
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Are fully connected layers necessary in a CNN?

There are mainly two main reasons for which we use FCN: If we use a fully connected layer for any classification or regression task, we have to flatten the results before transferring the information ...
Srajan's user avatar
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Must a CNN (both 1D and 2D) take input of the same size?

Yes padding is an option, but you can actually do better than that. Consider a convolutional layer: in order to define its parameters you only need to know the number of input channels, and the number ...
Alberto's user avatar
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CNN multioutput regression architecture modification

As far as I know, you can't get multiple outputs from keras.sequential and need as many output layers as outputs, creating branching into your NN. Something like this : ...
Samael's user avatar
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