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Interesting. What I like about the "Day to Night" example is that is clearly demonstrates that these sort of networks lack common sense. It expects light to be
by ragebol 10y ago
Interesting.
What I like about the "Day to Night" example is that is clearly demonstrates that these sort of networks lack common sense. It expects light to be where they are clearly (to humans with common sense at least) no things that can produce light.
E.g. in the middle of a roof or in a tree. Of course, there can be, but it's fairly uncommon.
And the opposite as well, no lights where a human would totally expect a light, eg. in the front of buildings or on the top of, well, lighting poles.
- drcode 10y agoI'd guess the problem is that the daytime pictures allow for easy feature detection (tree, building etc) but the nightime pictures are washed out- We humans look at the daytime picture first, then say "that nighttime picture must have a tree there" which involves feature detection across both pictures (in the training phase) I suspect a neural network better specialized for this task (i.e. that has the data interlaced for both day and nighttime during training) would have no problem feature detecting trees and leaving them unlit.