We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision.
Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers.
However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e. g., coloring a sketch of an originally blue car given a reference green car).
Exemplar-based colorization aims to add colors to a grayscale image guided by a content related reference im- age.