All Questions
8 questions
1
vote
0
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27
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Backtracking filter coefficients of Convolutional Neural Networks
I'm starting to learn how convolutional neural networks work, and I have a question regarding the filters. Apparently, these are randomly generated when the model is generated, and then as the data is ...
- 113
1
vote
0
answers
61
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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}^{...
7
votes
1
answer
360
views
Convolution backpropagation
I'm in the progress to learn, and understand different neural networks. I pretty much understand now feed-forward neural networks, and back-propagation of them, and now learning convolutional neural ...
- 109
3
votes
0
answers
123
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Computing derivatives for backpropagation across a convolution step
This will be a long post, but I hope it'll be instructive to anyone else in my position. I'm trying to find how the derivatives of the loss function are calculated with respect to the kernels and ...
- 299
3
votes
2
answers
218
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How to interpret sudden jumps of improvement in training error vs. iteration?
In the Residual learning paper by He et al., there are a number of plots of training/test error vs. backprop iteration. I've only ever seen "smooth" curves on these plots, while in this paper's ...
6
votes
1
answer
3k
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Adjusting weights in an convolutional neural network
I'm trying to implement a convolutional neural network at the moment. A simple feedforward network is not the problem but I'm having some trouble with the weight adjustment in the conv layer.
Lets ...
- 63
16
votes
1
answer
6k
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Back-propagation through max pooling layers
I have a small sub-question to this question.
I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was ...
- 263
6
votes
0
answers
293
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how to propagate error from convolutional layer to previous layer？
I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week.
To be specific, assume there are 3 layers in a convolutional pass, marked as ...
- 161