Mixing
Is it possible to output vectors/scalars from a neural network that are of a different type than the input
I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.
What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.
example of a set of out of phase signals
machine-learning neural-networks artificial-intelligence
asked Nov 20 '18 at 20:18
rbschris
11
add a comment |
I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.
What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.
example of a set of out of phase signals
machine-learning neural-networks artificial-intelligence
asked Nov 20 '18 at 20:18
rbschris
11
add a comment |
I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.
What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.
example of a set of out of phase signals
machine-learning neural-networks artificial-intelligence
asked Nov 20 '18 at 20:18
rbschris
11
I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.
What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.
example of a set of out of phase signals
machine-learning neural-networks artificial-intelligence
machine-learning neural-networks artificial-intelligence
asked Nov 20 '18 at 20:18
rbschris
11
asked Nov 20 '18 at 20:18
rbschris
11
asked Nov 20 '18 at 20:18
rbschris
11
asked Nov 20 '18 at 20:18
rbschris
11
asked Nov 20 '18 at 20:18
rbschris
11
11
add a comment |
add a comment |
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