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I have a set of paired binary images X,Y and an external function that I don't have access to, which receives X as input and outputs the binary image W.

I want to develop a neural network that can transform X into X_optimal such that after passing it to the external function I can minimize |Y-W|.

The external function can only receive binary images and it outputs binary images so X_optimal has to be another binary image, the problem with this is that normally the output of a neural network is between 0 or 1 and if I apply a threshold then I cannot backpropagate through the network because it is non-differentiable.

Please I would like to have some intuition on how to start my network architecture or if you know some papers that I can read it would be really helpful

desertnaut
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Marco Garcia Macias
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  • Please re-read [What topics can I ask about here?](https://stackoverflow.com/help/on-topic), and notice that recommendation requests for external resources, like papers etc, are explicitly off-topic here. – desertnaut May 14 '20 at 16:12

1 Answers1

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Your loss Function can be the NN output minus the expected output. In this case you can calculate the loss function of X_optimal and inverse Y (relative to your external function).

You can try to use a loss function of W-Y but, it might not converge with neural nets. You might want to try a XGBoost first.

rigo
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