Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [pooling]

For questions related to the pooling (aka downsampling or subsampling) operation/layers, in particular, in the context of convolutional neural networks. There are also specific tags for max-pooling and average pooling.

31 questions
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Can non-differentiable layer be used in a neural network, if it's not learned?

For example, AFAIK, the pooling layer in a CNN is not differentiable, but it can be used because it's not learning. Is it always true?
RedRus
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Is pooling a kind of dropout?

If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero. On the other hand, pooling, let's say max-pooling, takes the maximum value in a…
nsaura
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Can CNNs be applied to non-image data, given that the convolution and pooling operations are mainly applied to imagery?

When using CNNs for non-image (times series) data prediction, what are some constraints or things to look out for as compared to image data? To be more precise, I notice there are different types of layers in a CNN model, as described below, which…
nilsinelabore
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How many weights does the max-pooling layer have?

How many weights does the max-pooling layer have? For example, if there are 10 inputs, a pooling filter of size 2, stride 2, how many weights, including bias, does a max-pooling layer have?
Tibby
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What are the benefits of using max-pooling in convolutional neural networks?

I am reading Francois Chollet's Deep learning with Python, and I came across a section about max-pooling that's really giving me trouble. I am unable to copy-paste the content, so I've included screenshots of the paragraph that's troubling me. I…
An Ignorant Wanderer
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Is a non-linear activation function needed if we perform max-pooling after the convolution layer?

Is there any need to use a non-linear activation function (ReLU, LeakyReLU, Sigmoid, etc.) if the result of the convolution layer is passed through the sliding window max function, like max-pooling, which is non-linear itself? What about the average…
Kasia
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Is it effective to concatenate the results of mean-pooling and max-pooling?

Is it popular or effective to concatenate the results of mean-pooling and max-pooling, to get the invariance of the latter and the expressivity of the former?
user3180
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Is max-pooling really bad?

Hinton doesn't believe in the pooling operation (video). I also heard that many max-pooling layers have been replaced by convolutional layers in recent years, is that true?
user559678
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How do I choose the hyper-parameters for a model to detect different guitar chords?

I need to build a hand detector that recognizes the chord played by a hand on a guitar. I read this article Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation that looks like what I need (hand gesture…
Deffo
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What is the effect of using pooling layers in CNNs?

I know how pooling works, and what effect it has on the input dimensions - but I'm not sure why it's done in the first place. It'd be great if someone could provide some intuition behind it - while explaining the following excerpt from a blog: A…
stoic-santiago
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Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? If not, why do they perform as well as networks which use max-pooling?
FourierFlux
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Why do we have to dot product in the Low-rank Bilinear Pooling?

I was reading this paper Hadamard Product for Low-rank Bilinear Pooling. I understand what they are trying to say, but I don't know why we have to convert the element-wise multiplication into a scalar (using the dot…
user20755
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In which scenario would you want to have two adjacent pooling layers?

In which scenario, when assembling a CNN, would you want to have two adjacent pooling layers, without a convolutional layer in between?
Gaius
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Is down-sampling the only purpose of using stride?

Stride is used in at least two operations: convolution and pooling. Both operations can be viewed as applying a kernel function on input using a kernel (filter). Stride determines the amount of "jump" the kernel needs to perform on the input.…
hanugm
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Is there any reason behind bias towards max pooling over avg pooling?

Consider the following excerpt taken from the chapter named Using convolutions to generalize from the textbook titled Deep Learning with PyTorch by Eli Stevens et al. Downsampling could in principle occur in different ways. Scaling an image by half…
hanugm
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