Questions tagged [convolution]
For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.
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What are the general rules or principles for finding matrix operations that are used as filters in convolutional neural networks?
Is there a set of rules or guidelines for designing filters for convolutional neural networks? For example, a 3 x 3 layer with ones in the first column, zeroes in the second, and negative ones in the ...
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Shape of Flattened Layer in CNN
If I have a convolutional layer with dimension (5,5,4), (i.e, 4 no. of 5x5x1 feature maps), what will be the dimension of the flattened layer, if I apply flattening ...
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What neural network architecture would help me model a spectrogram?
I'm really a novice working with these technologies and I'm struggling to design a neural network that is powerful enough to model a spectrogram. For a personal project, I'm working on a spectrogram ...
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How does the use of 1x1 convolutional layers represent permutation in the GLOW model?
I am currently reading the GLOW paper (found here) and I can not understand how the authors claim that the use of 1x1 convolutional layers is equivalent to permutation holds true.
I understand how a ...
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How to align the description of a convolutional neural network in keras with wikipedia's conceptual model?
I was going through the introductory guide to convolutional neural networks in tensor flow here
And I was trying to logically map some of the code I saw to my actual understanding of how convolutional ...
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How is the number of channels in a convolutional layer shrinked or expanded?
I know in order to shrink or expand the number of channels a 1x1 convolution is performed.
I need to clarify the following: is the 1x1 convolution(s) just a matrix multiplication between the image ...
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How to backpropagate transposed convolution with stride and padding
Please, help! I have deadlines and I do not have time to figure out the topic on my own.
And now about the problem. I'm currently trying to figure out back propagation in transposed convolution. I ...
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Multi-channel convolution in Tensorflow
Suppose I have a sequence data of size $B \times N \times d$ where $B$ is the batch size, $N$ is the sequence length, and $d$ is the dimension or the number of features. Suppose I want to do 1D ...
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Transpose Convolution Output Size
I have been learning GAN (Generative Adversarial Networks) lately and having a hard time understanding the output size for transpose convolution. Let's say I am using a Tensor of [1, 64, 1, 1] as an ...
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Transposed Convolution in PyTorch Visualisation
I am trying to visualize the output of the transpose convolution in pytorch to better understand the operation. Here is my code, inputs and output images as well:
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How to select best kernel_size and max_pool_size in CNN1D
I have data with shape size 1,89. setup kernel_size = 3 and pool_size = 2 on the conv1d layer. However, the model is not able to predict the peak well. i think the problem is because the kernel_size ...
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CNN model why is ReLu used in Conv1D layer and in the first Dense Layer?
I have a problem. I have a CNN model which is used for an NLP problem. This is written in Python. I have questions about this, which I can't find an answer to.
Why is ReLu used inside the Conv1D ...
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Transpose Convolution feature extraction
Convolution extracts high-level features, but what about Transpose Convolution (or De/Up-Convolution)? Does it behave exactly the opposite? Does it generate lower-level features?
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why layer of dimension 1 is outputting image of size n
I am studying a model where landmarks from an image are calculated. The work comes from Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection.
I need to confirm why the ...
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NCHW input matrix to Dm conversion logic for convolution in cuDNN
I have been trying to understand the convolution lowering operation shown in the cuDNN paper. I was able to understand most of it by reading through and mapping various parameters to the image below. ...
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Inbetween CNN and MLP: neural network architecture for "close to convolutional" problem?
I am looking to approximate an (expensive to calculate precisely) forward problem using a NN. Input and output are vectors of identical length. Although not linear, the output somewhat resembles a ...
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Why does the 1st derivative appear to lag the slope of the fit in Scipy's Savitzky-Golay filter?
I have a simple script that performs the Savitzky-Golay filter on a toy dataset of forex prices from yahoo finance:
...
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How is image convolution actually implemented in deep learning libraries using simple linear algebra?
As a clarifier, I want to implement cross-correlation, but the machine learning literature keeps referring to it as convolution so I will stick with it.
I am trying to implement image convolution ...
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Scaling the output of a segmentation model (UNet)
So, I have to solve an instance segmentation problem and I am thinking of implementing a UNet model based on Ronneberger et. al. 2015 paper. The problem I have is that the output size has to be ...
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ResNet output dimensions of initial convolution don’t yield in an integer
I am trying to understand the ResNet dimensions, but got stuck at the first layer. We are passing a [224x224x3] image into 64 filters with kernel size 7x7 and stride=2. According to the ResNet source ...
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when depthwise separable convolution should be preferred over normal convolution?
As a novice in the realm of deep learning, I recently learned about Depthwise Separable Convolution. I have seen some tutorials and articles about it on internet, and in all of them the author ...
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Can a CNN have a different number of convolutional layers and kernel and what does it mean?
So if I have $3$ RGB channels, $6$ convolutional layers and $4$ kernels, does this mean that each kernel does a convolution on each channel and so the input for the next convolution will be $3 \times ...
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Why keras Conv2D makes convolution over volume?
I have a very basic question, but I couldn't get the idea about 2D convolution in Keras.
If I would create a model like this :
...
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Does a rotational convolutional filter exist in neural networks?
Traditionally, a convolutional filter is one where you take a matrix of numbers, multiply it with a subset of the data, and then sum it up. Then you move the filter left to right and top to bottom in ...
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How do convolutional layers in a CNN feed forward when there is multiple input feature maps?
I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in ...
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1x1 Convolution learnable parameters
Here is a code snippet wherein I add two convolution layers one with 3x3 filter followed by a layer with 1x1 filter. While I am sure how the parameters are calculated for 3x3 filter, I could not ...
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Why is the kernel of a Convolutional layer a 4D-tensor and not a 3D one?
I am doing my final degree project on Convolutional Networks and trying to understand the explanation shown in Deep Learning book by Ian Goodfellow et al.
When defining convolution for 2D images, the ...
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Performing 1D Depthwise conv using Keras 2D Depthwise conv
I would like to perform a 1D Depthwise convolution (ie the first step of the depthwise-separable convolution) for a machine learning model I am working on. This means that for an input activation ...
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How to calculate convolution for 2nd conv Layer in CNN, Do we need to average across all feature maps?
I understand that for the first layer (assuming we have a grayscale image) we calculate the convolution of 3*3 receptive field as a weighted sum of receptive weights with pixels
$ x1 · w1 + x2 · w2 + ...
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Understanding, visualizing and interpreting CNN activations
I am working with the first layer of a CNN and trying to understand how to interpret the activation output. My CNN takes input from 3 channels (RBG picture) and the first layer is ...
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Understanding scipy.signal.convolve2d full convolution and backpropagation between convolutional layers
I'm learning about convolutional neural networks. The convolution operation in order to extract features that is described in literature and posts used for this is quite intuitive and easy to ...
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What will be the input_shape of tf.keras.layers.Conv3D be for these inputs
I have many videos, and each video is made up of 37 images (there are 37 frames in the whole video). And the dimension of each image is (100, 100, 3).... So the ...
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Finding optimal time series using convolution [closed]
we logged sensor data while milling a workpiece. At several points, the workpiece was damaged and this induced a certain sensor data time series. Due to noise and since its a real world measurement, ...
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Trade-off between number of channels and size of convolutional filters
As far as I understand, the common practice in the modern CNN architectures is to use a smaller convolutional filters, but deeper networks with more channels.
One of the reason behind this is that one ...
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Are convolutions in deep learning associative?
Let's denote "convolution in deep learing" as "convolution-deep", and "convolution in math or signal processing" as "convolution-math".
As we all know, ...
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Padding in Convolution Formula
Why is it that the formula for each element in a convolution between an image $I$ and a $k \times k$ sized kernel $K$ is
$$ (I*K)_{ij}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{(i-m),(j-n)}K_{mn}=\sum_{m=0}^{...
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Does a Convolutional Layer in a Neural Network learn the correlation between its input signals via its kernel?
I am interested in the theory behing what a convolutional neural network learns with its convolutional operations. I think it learns (useful) kernels which measure the correlation between its input ...
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What features used by CNN model should a feature store actually store? [closed]
According to MLOPs principle, it is recommended to have a feature store. The question is in the context of doing image classification using deep learning models like convolutional neural networks ...
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Are 3D kernels in convolutions summed over their channels?
Say for example that I have a 28x28x1 grey scale image and I will perform two consecutive convolutions. The first convolution has 2 3x3x1 filters and the second has 3 3x3x2 filters. Each convolution ...
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Can I say that a trained neural network model with less parameters requires less resources during real world inference?
Let us imagine that we have two trained neural network models with different architectures (e.g., type of layers). The first model (a) uses 1D convolutional layers with fully-connected layers and has ...
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Comparison of different ways of Upsampling in detection models
There are various ways to increase the resolution of tensor in (width, height) dimensions, frequently used in detection models like ...
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Should kernel size always be a prime number?
Should kernel size always be a prime number? E.g. (3,3) (5,5) (7,7).
While tinkering with sklearn.preprocessing.KernelCenterer(), I noticed that I could only get it ...
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Error after merging two Deep Learning models VGG16 and ResNet50
I have merged two different models namely VGG16 and ResNet50 and given the outputs of the two models as input to another model. I have checked the Layers graph is correct. Before merging the code was ...
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Adapting ZFNet on 2244x224 image using a filter 7X7
I am building a model based on ZFNet in Tensorflow 2.0. I am using the Petal images dataset. The images are of size 224x244x3.
So my question is when implementing the first layer (conv2d) with filter ...
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There are 2 figures explaining transposed convolution. Which one is correct?
I have been struggling to understand transposed convolution. When I search for "transposed convolution", there are 2 figures explaining transposed convolution in which I think are not ...
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ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 25, 25, 1]
I am trying to use conv1D but getting that error.
My dataset's is batched and has a shape of [None, 25, 25, 1]
I am using input_shape=(25,25)
I am not able to figure out what should I change so I can ...
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Can anyone recommend me a very good pre-trained model for face or head detection?
I really need to know the best pre-trained models to detect faces and/or peoples' head. Not a face recognition model, but only to classify whether an object is a person's head/face or not.
I'm ...
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Can convolutional network learn structural properties of one feature w.r.t to other?
I'm going through the literature on pose-estimation ( DeeperCut, OpenPose, MultiPersonPosetrack).
I'm interested in knowing whether these networks/ generally a CNN can learn properties (geometrical) ...
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What does it mean to say convolution implementation is based on GEMM (matrix multiply) or it is based on 1x1 kernels?
I have been trying to understand (but miserably failing) how convolutions on images (with height, width, channels) are implemented in software.
I've heard people say their convolution implementation ...
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