Combine two neural networks with different inputs through element-wise summation of certain layers
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I am looking at combining two Convolutional Neural Networks into one through element-wise summation of activation functions. Both these networks have different inputs, but are similar in their architecture.
I have seen from certain papers and github pages that this has been, successfully, implemented in Python. However, I was wondering if this would also be possible to implement in MATLAB?
One example of what I want to reproduce is the FuseNet architecture by Hazirbas et al. https://github.com/zanilzanzan/FuseNet_PyTorch:
Is it possible to reproduce this in MATLAB, and if so, how do I start?
matlab neural-network computer-vision classification semantic-segmentation
edited Nov 8 at 14:16
Dev-iL
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
add a comment |
up vote
0
down vote
favorite
I am looking at combining two Convolutional Neural Networks into one through element-wise summation of activation functions. Both these networks have different inputs, but are similar in their architecture.
I have seen from certain papers and github pages that this has been, successfully, implemented in Python. However, I was wondering if this would also be possible to implement in MATLAB?
One example of what I want to reproduce is the FuseNet architecture by Hazirbas et al. https://github.com/zanilzanzan/FuseNet_PyTorch:
Is it possible to reproduce this in MATLAB, and if so, how do I start?
matlab neural-network computer-vision classification semantic-segmentation
edited Nov 8 at 14:16
Dev-iL
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am looking at combining two Convolutional Neural Networks into one through element-wise summation of activation functions. Both these networks have different inputs, but are similar in their architecture.
I have seen from certain papers and github pages that this has been, successfully, implemented in Python. However, I was wondering if this would also be possible to implement in MATLAB?
One example of what I want to reproduce is the FuseNet architecture by Hazirbas et al. https://github.com/zanilzanzan/FuseNet_PyTorch:
Is it possible to reproduce this in MATLAB, and if so, how do I start?
matlab neural-network computer-vision classification semantic-segmentation
edited Nov 8 at 14:16
Dev-iL
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
I am looking at combining two Convolutional Neural Networks into one through element-wise summation of activation functions. Both these networks have different inputs, but are similar in their architecture.
I have seen from certain papers and github pages that this has been, successfully, implemented in Python. However, I was wondering if this would also be possible to implement in MATLAB?
One example of what I want to reproduce is the FuseNet architecture by Hazirbas et al. https://github.com/zanilzanzan/FuseNet_PyTorch:
Is it possible to reproduce this in MATLAB, and if so, how do I start?
matlab neural-network computer-vision classification semantic-segmentation
matlab neural-network computer-vision classification semantic-segmentation
edited Nov 8 at 14:16
Dev-iL
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
edited Nov 8 at 14:16
Dev-iL
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
edited Nov 8 at 14:16
Dev-iL
16.1k63974
edited Nov 8 at 14:16
Dev-iL
16.1k63974
edited Nov 8 at 14:16
Dev-iL
16.1k63974
16.1k63974
asked Nov 8 at 11:13
Isa El Doori
91
asked Nov 8 at 11:13
Isa El Doori
91
asked Nov 8 at 11:13
Isa El Doori
91
91
add a comment |
add a comment |
1 Answer
1
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up vote
0
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You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:
The element-wise summation, specifically, can be performed using an additionLayer.
answered Nov 8 at 13:56
Dev-iL
16.1k63974
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:
The element-wise summation, specifically, can be performed using an additionLayer.
answered Nov 8 at 13:56
Dev-iL
16.1k63974
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
add a comment |
up vote
0
down vote
You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:
The element-wise summation, specifically, can be performed using an additionLayer.
answered Nov 8 at 13:56
Dev-iL
16.1k63974
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
add a comment |
up vote
0
down vote
up vote
0
down vote
You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:
The element-wise summation, specifically, can be performed using an additionLayer.
answered Nov 8 at 13:56
Dev-iL
16.1k63974
You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:
The element-wise summation, specifically, can be performed using an additionLayer.
answered Nov 8 at 13:56
Dev-iL
16.1k63974
answered Nov 8 at 13:56
Dev-iL
16.1k63974
answered Nov 8 at 13:56
Dev-iL
16.1k63974
answered Nov 8 at 13:56
Dev-iL
16.1k63974
16.1k63974
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
add a comment |
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
Hi Dev-iL, I was able to reproduce this network. However, since the DAGNetwork only allows for one input, my question then becomes: would it be possible to have a 4-channel input, 3 channels go to one side of the network, and the remaining channel goes to the other side of the network?
– Isa El Doori
Nov 8 at 17:43
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
@IsaElDoori I don't know of an easy way to achieve that. What you could do, is define a custom layer that does this "unzipping". Basically the reverse of a depth concatenation layer. Either that, or a custom layer that all it does is forward certain dimensions (or slices) of the input forward.
– Dev-iL
Nov 8 at 18:55
add a comment |
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