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:



FuseNet NN architecture by Hazirbas et al.



Is it possible to reproduce this in MATLAB, and if so, how do I start?






matlab neural-network computer-vision classification semantic-segmentation







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    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:



    FuseNet NN architecture by Hazirbas et al.



    Is it possible to reproduce this in MATLAB, and if so, how do I start?






    matlab neural-network computer-vision classification semantic-segmentation







    share|improve this question


























      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:



      FuseNet NN architecture by Hazirbas et al.



      Is it possible to reproduce this in MATLAB, and if so, how do I start?






      matlab neural-network computer-vision classification semantic-segmentation







      share|improve this question















      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:



      FuseNet NN architecture by Hazirbas et al.



      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






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      edited Nov 8 at 14:16









      Dev-iL

      16.1k63974




      16.1k63974










      asked Nov 8 at 11:13









      Isa El Doori

      91




      91
























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          You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:



          enter image description here



          The element-wise summation, specifically, can be performed using an additionLayer.






          share|improve this answer





















          • 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











          Your Answer






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          1 Answer
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          active

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          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:



          enter image description here



          The element-wise summation, specifically, can be performed using an additionLayer.






          share|improve this answer





















          • 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















          up vote
          0
          down vote













          You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:



          enter image description here



          The element-wise summation, specifically, can be performed using an additionLayer.






          share|improve this answer





















          • 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













          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:



          enter image description here



          The element-wise summation, specifically, can be performed using an additionLayer.






          share|improve this answer












          You might be able to do this using a DAG network1,2 in MATLAB. Here's an illustration:



          enter image description here



          The element-wise summation, specifically, can be performed using an additionLayer.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          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


















          • 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


















           

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