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Questions tagged [convolution]

For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.

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Why convolution with mode "padding = same" perfoms better than convolution without padding?

I have tried both regimes and padding = same performs a slightly better (increases up to 2-5% accuracy for example for cifar10 dataset)
Тима 's user avatar
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1 answer
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Output of a convolutional layer

Is the calculated output correct?
PeterBe's user avatar
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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 ...
Joachim Rives's user avatar
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1 answer
166 views

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 ...
mainak mukherjee's user avatar
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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 ...
BOBONA's user avatar
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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 ...
Faraz M.'s user avatar
1 vote
1 answer
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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 ...
Sidharth Ghoshal's user avatar
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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 ...
alexandrosangeli's user avatar
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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 ...
James's user avatar
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1 vote
1 answer
144 views

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 ...
poglhar's user avatar
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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 ...
pwnkit's user avatar
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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 ...
Test's user avatar
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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?
canP's user avatar
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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 ...
Asad's user avatar
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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. ...
Rajesh Shashi Kumar's user avatar
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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 ...
Mav's user avatar
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1 vote
1 answer
384 views

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: ...
quant's user avatar
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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 ...
Jozef Nagy's user avatar
1 vote
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83 views

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 ...
abhishek bhatt's user avatar
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1 answer
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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 ...
Malte's user avatar
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3 votes
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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 ...
K327's user avatar
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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 ...
plastico's user avatar
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1 answer
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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 : ...
user52219's user avatar
1 vote
1 answer
205 views

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 ...
xiaodai's user avatar
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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 ...
Joth's user avatar
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1 answer
126 views

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 ...
Ananth Subramanian's user avatar
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1 answer
1k views

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 ...
puradrogasincortar's user avatar
0 votes
1 answer
316 views

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 ...
Karl Haebler's user avatar
0 votes
1 answer
466 views

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 + ...
A.B's user avatar
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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 ...
User2321's user avatar
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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 ...
Julen's user avatar
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1 vote
1 answer
1k views

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 ...
user avatar
1 vote
0 answers
19 views

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, ...
MaxMotzer's user avatar
1 vote
0 answers
94 views

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 ...
spiridon_the_sun_rotator's user avatar
1 vote
0 answers
53 views

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, ...
WBR's user avatar
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1 vote
0 answers
60 views

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}^{...
dontloseyourgoalie's user avatar
3 votes
1 answer
148 views

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 ...
user3352632's user avatar
0 votes
1 answer
273 views

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 ...
GeorgeOfTheRF's user avatar
1 vote
0 answers
32 views

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 ...
dontloseyourgoalie's user avatar
1 vote
0 answers
30 views

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 ...
user3352632's user avatar
1 vote
0 answers
515 views

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 ...
spiridon_the_sun_rotator's user avatar
0 votes
1 answer
103 views

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 ...
Kermit's user avatar
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1 vote
1 answer
510 views

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 ...
ALI TARIQ NAGI's user avatar
1 vote
1 answer
38 views

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 ...
Dawood Aijaz's user avatar
1 vote
0 answers
113 views

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 ...
Mathew's user avatar
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2 votes
2 answers
47k views

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 ...
Lukas's user avatar
  • 141
0 votes
1 answer
1k views

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 ...
Maf's user avatar
  • 125
1 vote
0 answers
17 views

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) ...
amarykya_ishtmella's user avatar
1 vote
0 answers
152 views

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 ...
Joe Black's user avatar
  • 123
1 vote
0 answers
332 views

Understanding image size changes in DCGAN

I have been studying and trying to implement Generative Adversarial Networks using PyTorch. More precisely I tried to replicate the DCGAN PyTorch Tutorial tutorial using some custom dataset. My code ...
Moonstone5629's user avatar

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