This is a PyTorch implementation of Unsupervised Diverse Colorization via Generative Adversarial Networks which makes use of batch normalization and conditional concatenation to improve upon other published implementations in terms of training efficiency and evaluation results.
A pair of neural networks are trained in an alternating manner to learn a conditional mapping from a single channel grayscale image to a three-channel colorized image. The HSV cylindrical-coordinate encoding is used rather than RGB as it only requires the networks to learn 2 other channels rather than three. The mapping learned thus entails learning the hue and saturation channels given the value channel.
A pair of generative and discriminative models are trained end to end. The generator is given uniform noise as input and is conditioned on the value channel of some given image. This conditioning gives rise ot the conditional GAN architecture. Unlike former image colorizer proposals, this generator is conditioned on the input at each convolutional block. This vastly improves the training procedure.
| Input | Output | Target |
|---|---|---|
python train.py
python3 eval.py