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 Recognition. In the proposed architecture of the model, a MaxPooling Window:1 × 2, s:2 layer is mentioned. I'm not sure what the size of the output of this layer would be. If i have an input of size (32 x 8), then the output would be: (32-1)/2 + 1 = 16.5, …

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. As I understand it, this way of pooling may lose some of the finer details, especially in big images for which the pooling is rougher. Is my intuition correct and is there any other way of retrieving a fixed sized feature vector? Technical details: …

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 order to convert the tensor into the shape(32, 32, 256, 256). In PyTorch, if I try to apply the pooling then the last two dimensions of the shape, related to the image, are changing, but not the …

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 and output channels is same as pooling layers processes each channel independently and thus produces as many output channels as there are in the input. But in the second slide, the number of output and input channels of the MAX-POOL is different: number of input channels …

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 both positive and negative anomalies. The implementation i have tested employs a number of 2D-convolutional layers, followed by max-pooling. Max-pooling naturally has a bias towards positive values. Does it mean that negative anomalies in my images are going to be given less wight in the …

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 kernel_size to the input dimension or to use torch.mean()?

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). E.g. converts many embeddings (n × m) to a list of means (1 × m). However, these all loose a lot of information (especially the relationships between features in single embeddings). This doesn't have to be a reduction (i.e. the resulting 1D vector can be larger than m). If it's …

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 layer? Or is it an error to use in the first layer?

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 not be captured by the 'pooled' representation. The 'pooled' representation will tend to be similar to the most frequent features. It will not capture the 'max' feature. Could you please explain this method.

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 utilizes 3D convolutions on the encoding side and 2D (de)convolutions on the decoding side. Through the skip connections I have been utilizing a 3D max-pooling to collapse the hyperspectral band dimension, $b$, to $1$ so that I may keep the receptive field and structural information through the …

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 : import keras import tensorflow as tf import tensorflow.keras.layers as L import tensorflow.keras.models as M import tensorflow.keras.callbacks as C import tensorflow.keras.utils as U def make_model_lstm_pooling(inshape=50000): z = L.Input(shape=(inshape, 10)) x = L.AveragePooling1D(pool_size=1, strides=100)(z) x = L.Bidirectional( L.LSTM(10, dropout=0.1, return_sequences=False, kernel_initializer='ones', bias_initializer='zeros') )(x) x = L.Dense(10, activation='linear')(x) x = L.Dense(1, activation='linear')(x) …

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 network I feel that these are not real reasons for using a pooling layer because There is no real need to select important features because the fully connected layer at the very end could be used to identify the most important features The …

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, are some nodes of the input set to zero? If that so how does this differ from max-pooling-dropout? Even in max-pooling-dropout some elements in the input are randomly dropped (Set to zero).

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 learnt for pooling layer but for eg. max pool can have different values at different iterations? Will that effect the error surface?

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 MaxPooling layers with padding. If padding=same in convolutional layers, our output size (at least height and width, depth can change based on the number of filters) is the same as the input. I expected the same with the MaxPooling layer, but Keras model.summary() (as shown in the article) shows that the output …

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 it worth it to remove pooling layers or not? does it lead to a higher accuracy? Have you ever seen any relevant papers, articles, etc. about this issue? (I've searched and couldn't find much things except for this paper) I even don't know how many pooling layers should I remove.