Questions tagged [pooling]

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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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0answers
30 views

Mathematical description of max pooling operation

As described in the paper "The all convolutional net", pooling can be described with the following formula (see image). With p → ∞ (maximum norm) it becomes the max pooling operation. Every ...
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1answer
24 views

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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258 views

global average pooling in PyTorch: torch.nn.AvgPool1d vs torch.mean

To implement global average pooling in a PyTorch neural network model, which one is better and why: to use torch.nn.AvgPool1d() and set the ...
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13 views

How do you aggregate features of lists (pooling alternatives)?

Is it possible to reduce non-correlated multi-dimensional data over features to 1D data? A working option is pooling (mean/min/max) over an embedding vector (n samples of embeddings of m dimensions). ...
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1answer
85 views

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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0answers
19 views

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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1answer
4k views

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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1answer
245 views

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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0answers
33 views

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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0answers
15 views

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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1answer
242 views

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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2answers
4k views

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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5answers
433 views

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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1answer
281 views

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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187 views

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 ...