Instead of looking for patterns from itself on others images (training data) it starts with noise and deforms such noise based on patterns found on itself and favors deformations closer to the input image; eventually reaching something close to the input image without the noise (cause the noise it's pattern-less or at least weak enough to die over stronger patterns)
Does this say more about CNNS fundamentally or our design process for their structure? What sort of assumptions might one make when "structuring" a CNN?
