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Typical CNNs can learn a linear transformation of the input at no cost. Since YUV is such a linear transformation of RGB, there is no benefit in converting to i
by CodesInChaos 3y ago
Typical CNNs can learn a linear transformation of the input at no cost. Since YUV is such a linear transformation of RGB, there is no benefit in converting to it beforehand.
- bob1029 3y agoHow is there not a cost associated with forcing the machine to learn how to do something that we already have a simple, deterministic algorithm for? Won't some engineer need to double check a few things with regard to the AI's idea of color space transform?
- uoaei 3y agoYour instincts are correct. Training is faster, more stable, and more efficient that way. In certain cases it "pretty much is irrelevant" but the advantages of the strategy of modelling the knowns and training only on the unknowns becomes starkly apparent when doing e.g. sensor fusion or other ML tasks on physical systems.
- CodesInChaos 3y agoYou could probably derive some smart initialization for the first layer of a NN based on domain knowledge (color spaces, sobel filters, etc.). But since this is such a small part of what the NN has to learn, I expect this to result in a small improvement in training time and have no effect on final performance and accuracy, so it's unlikely to be worth the complexity of developing such a feature.
- whimsicalism 3y agoAbsolutely this. Seems like on HN people are still learning 'the bitter lesson'.
- CSSer 3y agoAmdahl’s law?
- nl 3y agoRick Sutton. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- CSSer 3y agoThank you!
- whimsicalism 3y agoSorry - should have included a cite. That said, Amdahl's law is also probably related in some degree - I would view YUV conversion as an unnecessary optimization.