Lots of Machine Learning methods are inspired Biology, Nature, Physics, Neurology...
I just thought of a Deep Learning approach inspired on religion:
Some eastern religions state that our soul has a deeper purpose and it's immortal. When we are born, we briefly forget this purpose so we can expand further our consciousness. (Something like this, I don't really know)
Anyway, I was thinking on a DNN based on this concept. For explicit comparison, the soul would be a deeper layer (very slow training due to Vanishing Gradient problem). The "shallow" layers would be our body, living some few experiences, learning until converge and than dying.
Hypothesis / Question:
- If we reset the shallow layers periodically. Could that somehow lead to a better deep layer on the long run?
- Could this approach be actually useful in any scenario?
- If that's a bad idea, can you explain me why?
Consider any scenario where it could be useful, for example:
- Run each "reincarnation" with a different dataset, or even topology
- Adding / removing ResNet to the model
- Setting the Reset condition based on training convergence