Questions tagged [max-pooling]

For questions related to the max-pooling operation, where the maximum of a set of numbers is chosen as the "representative" of that set of numbers, which is commonly used in convolutional neural networks.

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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's user avatar
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1 answer
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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's user avatar
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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 ...
An Ignorant Wanderer's user avatar
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1 answer
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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, ...
Kasia's user avatar
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2 answers
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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 I also heard that many max-pooling layers have been replaced by convolutional layers in recent years, is that true?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's user avatar
3 votes
3 answers
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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's user avatar
3 votes
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What is the fundamental difference between max pooling and adaptive max pooling used in PyTorch

PyTorch provides max pooling and adaptive max pooling. Both, max pooling and adaptive max pooling, is defined in three dimensions: 1d, 2d and 3d. For simplicity, I am discussing about 1d in this ...
hanugm's user avatar
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1 answer
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How is the max function differentiable wrt multiple arguments?

I recently came across an answer on StackOverflow that mentioned the max function being differentiable with respect to its values. From my current understanding of ...
Peyman's user avatar
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2 answers
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How translation invariance is achieved in CNNs?

I am trying to understand how translation invariance is achieved in CNNs. For example, consider the following simple binary classification problem: predicting whether the letter that appears on an ...
ado sar's user avatar
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2 votes
1 answer
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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 ...
hanugm's user avatar
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How can we compute the gradient of max pooling with overlapping regions?

While studying backpropagation in CNNs, I can't understand how can we compute the gradient of max pooling with overlapping regions. That's also a question from this quiz and can be also found on this ...
estamos's user avatar
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How can max-pooling be applied to find features in words?

I'm reading about max-pooling in a dynamic CNN paper. I can see how it can help find features in images, given that the pixel with the highest density gets pooled, but how does it help to find ...
user8714896's user avatar
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Why are convolutions and pooling described as layers in a network?

Whenever I look at resources on convolutions and max-pooling in CNNs they always seem to describe these algorithms as being part of the network - a preliminary set of layers before the main ...
Gamaray's user avatar
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