Show HN: Open source colorizing grayscale images with NN
These results: <a href="http://richzhang.github.io/colorization/" rel="nofollow">http://richzhang.github.io/colorization/</a> from Berkeley are <i>much</i> better than this model (and the code is open source as well).<p>Furthermore, those better results have the advantage of a much simpler model. This model has a fairly complicated architecture (a complex residual concatenation setup) and many more parameters (I would guess anywhere between 2x-10x as many, but I'd have to take a closer look), which means it's much slower to run and takes up more memory (disk and RAM).<p>I'd also say that in general the better model does things that are a lot more common sense: using the CIE Lab color space (perceptually uniform), omitting pooling, using a classification loss instead of regression (regression in generally performs poorly in deep learning), etc.