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.

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Order of multiple Convolutional and Pooling layers in generated CNNs

I am reading this article: https://www.sciencedirect.com/science/article/pii/S2210650221000249 There, a multi layered particle swarm optimization of CNN parameters is presented. First step (layer) is ...
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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 "...
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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 ...
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How to select dimensions of kernel and stride for pooling?

Consider a tensor of size $512 \times 512$. I need to reduce it to $32 \times 32$. There are several ways to do it. There are a lot of possibilities. Each possibility has its own kernel dimensions and ...
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Is there any recommended way to perform pooling in this context?

Suppose I have three batches of feature maps, each of size $180 \times 100 \times 100$. I want to concatenate all these feature maps channel-wise, and then resize them into a single feature map. The ...
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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 ...
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Is there any closed form analytical expression to represent fractional max pooling?

There are Nineteen types of pooling layers in PyTorch. Almost all of the layers are provided with corresponding analytical formulae. But analytical formulae are not provided for the fractional max-...
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DeepLabV3: Why use global average pooling in the ASPP module?

I'm trying to understand the rationale of the various modifications the authors of the DeepLab models have made to their third version, DeepLabV3. In the paper, the following is written: ASPP with ...
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Use case for dilated pooling operator used in a Machine Learning model?

I have seen that most of the deep-learning frameworks have the ability to do dilated pooling. Many frameworks have recently been updated to add the dilated property to the pooling. However, I have not ...
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What are the purposes of pooling in CNNs?

There are at least three questions on this site related to this What is the effect of using pooling layers in CNNs? Is pooling a kind of dropout? What are the benefits of using max-pooling in ...
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How do you pass the image from one convolutional layer to another in a CNN?

I am currently trying to write a CNN from scratch, but I don't understand how to feed the information from a max-pooling layer to the next convolutional layer. Specifically, I don't know what to do ...
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Is average pooling equivalent to a strided convolution with a specific constant kernel?

It seems to me that average pooling can be replaced by a strided convolution with a constant kernel. For instance, a 3x3 pooling would be equivalent to a strided convolution (of stride $3$) with a $3 \...
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What is the time complexity of the upsampling stage of the U-net?

I am trying to determine the complexity of the neural network we use. The neural network is a U-net generator with an input shape of NxN (not an image but image-like data) and output of the same shape....
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How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?

I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was ...
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2 answers
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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 ...
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1 answer
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In Fast R-CNN, how are input RoIs mapped to the respective RoIs in the feature map before RoI pooling?

I've been reading the Fast R-CNN paper. My understanding is that the input to one forward pass is the whole input image plus a list of RoIs (generated by selective search or another region proposal ...
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What's the difference in using multiple convolutional layers and no pooling versus using a single convolutional layer and a single max pooling layer?

I'm currently working on a college project in which I'm designing a Deep Q-Network that takes images/frames as an input. I've been searching online to see how other people have designed their ...
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3 votes
3 answers
310 views

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?
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1 answer
2k views

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, ...
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2 votes
2 answers
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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 ...
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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 ...
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1 vote
1 answer
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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 ...
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4 votes
1 answer
934 views

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?
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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?
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5 votes
1 answer
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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 ...
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4 votes
2 answers
990 views

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?
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4 votes
2 answers
2k views

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-...
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2 votes
1 answer
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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 ...
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0 votes
1 answer
93 views

In the inception neural network, how is an image of shape $224 \times 224 \times 3$ converted into one of shape $112 \times 112 \times 64$?

According to the original paper on page 4, $224 \times 224 \times 3$ image is reduced to $112 \times 112 \times 64$ using a filter $7 \times 7$ and stride $2$ after convolution. $n \times n = 224 \...
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9 votes
1 answer
3k views

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?
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1 answer
300 views

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?
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