A recent HN submission on physics talked about discovering hidden symmetries of gravity. Somehow this seems a little bit similar. Neural networks have a great

A recent HN submission on physics talked about discovering hidden symmetries of gravity. Somehow this seems a little bit similar. Neural networks have a great many parameters. If training on very abstract images gives good results, then it seems like it indicates that there are certain symmetries that makes the neural network better, irrespective of dataset or task. If that hypothesis is true, then it may be possible to change the architecture of the network to directly provide those properties without any training at all.