Final Project in Advanced Machine Learning 2019
In this repo we looked at the colorization problem from two different angles and compare them in our report.
- Using a Generative Adversarial Networks
- Using the classification approach
To train a model execute a line like the following but replace train.py with the corresponing python file (train_gan.py or train_classification.py)
python train.py -n NAME
Here are some results where the colorization was successful.
Left: Classification, right: GAN
The order is from left to right: Grayscale input image, ground truth image, colorized version
The images were taken from the STL-10 testset.