5 votes
Accepted

Is a non-linear activation function needed if we perform max-pooling after the convolution layer?

Let's first recapitulate why the function that calculates the maximum between two or more numbers, $z=\operatorname{max}(x_1, x_2)$, is not a linear function. A linear function is defined as $y=f(x) =...
  • 35k
3 votes

Is max-pooling really bad?

In addition to JCP's answer I would like to add some more detail. At best, max pooling is a less than optimal method to reduce feature matrix complexity and therefore over/under fitting and improve ...
3 votes

Is it effective to concatenate the results of mean-pooling and max-pooling?

I haven't seen it as you describe and I don't think it would be much useful. Pooling layers are being gradually phased out of networks, because they don't seem to be that useful anymore. With the ...
  • 131
2 votes

Is there any reason behind bias towards max pooling over avg pooling?

I've found out rather a good explanation on Quora. Max pooling extracts the most salient features - edges, cusps, whatever. Average pooling operates smoothly - collects features, that are relevant to ...
2 votes

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Neural networks are not invariant to translations, but equivariant, Invariance vs Equivariance Suppose we have input $x$ and the output $y=f(x)$ of some map between spaces $X$ and $Y$. We apply ...
2 votes
Accepted

How can we compute the gradient of max pooling with overlapping regions?

When gradients in a neural network can follow multiple paths to same parameter, the different gradient values from the sources can often be added together, because the operations in the forward ...
  • 24.7k
1 vote

How can max-pooling be applied to find features in words?

In an image you are pooling usually over some (n x n) set of positions which lets you maintain spatial correlation but on the other hand most 1D CNNs used for language modeling pool over the temporal ...
  • 2,309
1 vote
Accepted

How many weights does the max-pooling layer have?

A max-pooling layer doesn't have any trainable weights. It has only hyperparameters, but they are non-trainable. The max-pooling process calculates the maximum value of the filter, which consists of ...
  • 1,715
1 vote

What are the benefits of using max-pooling in convolutional neural networks?

MaxPooling pools together information. Imagine you have 2 convolutional layers $(F_1, F_2)$ respectively, each with a 3x3 kernel and a stride of $1$. Also, imagine your input is $I$ is of shape $(w,h)$...
  • 2,309
1 vote

Is max-pooling really bad?

Max pooling isn't bad, it just depends of what are you using the convnet for. For example if you are analyzing objects and the position of the object is important you shouldn't use it because the ...
  • 153

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