We include colorizations of black and white photos of renowned photographers as an interesting “out-of-dataset” experiment and make no claims as to artistic improvements, although we do enjoy many of the results!
This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations.
We embrace the underlying uncertainty of the problem by posing it as a classification task and explore using class-rebalancing at training time to increase the diversity of colors in the result.
The system is implemented as a feed-forward operation in a CNN at test time and is trained on over a million color images.
Categories are ranked according to the difference in performance of VGG classification on the colorized result compared to on the grayscale version.
See original article for details