[D] Dynamic routing by feedback activation maximization • r/MachineLearning

I'm wondering if there has been any work done on CNNs with each filter pooler trying to match a (scalar) value fed back from the most immediate downstream neuron, where this feedback value is ultimately anchored to some cost function on the top layer. I'm imagining a simple architecture where the goal is to derive the type of "inverse graphics" transformation that Hinton speaks of. The top layer feedback values could even be some affine transformation of the input data. The trickling down of activation values would be performed in iterations and in a diffusion process induce a kind of dynamic routing with neighboring filters forced to agree upon a constellation of transformations. After some number iterations, fixed or based on some local maxima convergence criteria, the diffusion would be halted and the weights updated in standard backprop fashion.