  In recent years, the majority of works on deep-learning-based image
colorization have focused on how to make a good use of the enormous datasets
currently available. What about when the data at disposal are scarce? The main
objective of this work is to prove that a network can be trained and can
provide excellent colorization results even without a large quantity of data.
The adopted approach is a mixed one, which uses an adversarial method for the
actual colorization, and a meta-learning technique to enhance the generator
model. Also, a clusterization a-priori of the training dataset ensures a
task-oriented division useful for meta-learning, and at the same time reduces
the per-step number of images. This paper describes in detail the method and
its main motivations, and a discussion of results and future developments is
provided.
