Machine Learning Idea – Per Layer Error Correction L1 = Denoise(L1), L2 = Denoise(L2), … Criterion(output,target)

I’m trying to figure out a super battery. The philosophical abstract part. In that I’m using machine learning.

Maybe you need better machine learning for a super battery. So here goes.

The idea is to have “error correction” on each layer. So what error inputs do we have if we only got a target in the last layer?

What I guess is that you can you compare each layer with a ?image denoise filter. This way you get a layer that approaches denoise(layer). I will try assigning L1 = Denoise(L1) and so on.

With this I think I took some convergence problems of some many layer models.

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