Questions tagged [pooling]

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What to do with Transformer Encoder output?

I'm in the middle of learning about Transformer layers, and I feel like I've got enough of the general idea behind them to be dangerous. I'm designing a neural network and my team would like to ...
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Should every CNN layer be followed by a pooling layer?

I am working with simulated univariate sequential data and the goal is to forecast that data using a CNN-LSTM model. I know that a convolutional layer is mostly followed by a pooling layer like ...
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Is RoI Pooling appropriate to retrieve fine details from objects of varying sizes?

I’m using a RoI Pooling after a CNN that extract features from images of varying sizes, containing defects I want to classify. The images and defects sizes range from a couple of pixels to ~100 pixels....
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Number of input and output channels of MAX POOL layer

This is what Andrew Ng draws in his pooling layers video in the Coursera Deep Learning Specialization: and this is what he draws in Inception network video: Notice in first slide, number of input ...
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Is it possible to apply pooling across the channel dimension of the input tensor?

I have an input tensor of the shape (32, 256, 256, 256). In this tensor shape, 32 is the batch size. second 256 is the number of channels in the given image of size 256 X 256. I want to do pooling in ...
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Max Pooling in first Layer of CNN

I am seeing, in all the notebooks that I found, that Max Pooling is never used in the first layer of a CNN. Why this? Is it a convention among data scientist to do not use max pooling in the first ...
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Utilizing 1x1(x1) convolutions as a learned max pooling (3D)?

I have a semantic segmentation network that ingests 3D images (hyperspectral $(x, y, b)$) and predicts 2D images (semantic map $(x, y)$). This network takes the form of a classic UNet, though it ...
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ValueError: No gradients provided for any variable

I have this error when running training on my model. I found this issue on different sites, but could not find a solution to my problem. Here is my model : ...
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Absolute-value max pooling in 2D convolutional neural networks

i am fairly new to machine learning, so this may be a silly question. if that is the case, I apologise in advance. i am training a convolutional neural network on oceanographic images, which include ...
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What is dropout in convolutional layers and how does that different from max-pooling-dropout?

When dropout is applied to fully connected layers some nodes will be randomly set to 0. It is unclear to me how dropout work with convolutional layers. If dropout is applied before the convolutions, ...
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The idea behind Generalized Max Pooling

I am trying to understand the idea of "Generalized Max Pooling". It seems they try to make the 'pooled' representation similar to the features. If so I feel some rare discriminating features could ...
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How gradients are learned for pooling layers in Convolution Neural Network?

Assuming we could compute a layerwise Hessian of the error function when training a neural network, the error sub-surface of pooling layers will be flat.?? Is that correct? There is no weights to be ...
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What's the purpose of padding with Maxpooling?

As mentioned in the question, i've noticed that sometimes there are pooling layers with padding. More specifically, I found this Keras tutorial, where there's a net which contains ...
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Real purpose of pooling

Recently I had a doubt as to what is the real purpose of pooling layers in neural networks is? The most common answer is To select the most important feature To increase the receptive field of the ...
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How is the output of a maxpool layer window size=1x2 and stride=2 calculated?

I'm looking at the architecture proposed in the following paper: Baoguang Shi et al, An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text ...
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The effect of removing pooling layers in the model's accuracy

I know that removing pooling layers will lead to an increase in dimensionality and subsequently, make the training to be more time-consuming. But I'm wondering if ...
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