All Questions
Tagged with neural-network convolution
104
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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 ...
- 111
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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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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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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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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 ...
user113403
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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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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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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 ...
- 123
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Strategy for improving performance of 3D convolutional GAN
Others working with neural nets and GAN's might find this question interesting.
Background:
I've been working with data from Berkeleys PEER Ground Motion Database to generate new novel seismic traces. ...
- 121
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Can I tune a model after training it? (Convolutional Neural Network & Classification)
I am relatively new to Data Science and I've recently embarked on a project. Long story short, I've trained a CNN model to distinguish between Male and Female genders. However, I wish to tune my model....
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How to perform upsampling (and NOT interpolation) process theoretically modelled?
As an example, I know that sampling a signal $s$ is modelled by multiplication of s by a dirac comb, which has the effect of convolving the Fourier Transform (FT) of $s$ by the FT of the dirac comb ...
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