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 clusterizationa 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.
In the following, results obtained using only the cGAN are shown. Each group of three images is composed of the input of the network (grayscale image), the ground truth, and the output of the network.
In the following, results of MetalGAN are shown. Each of three images consists of the grayscale input given to the network, the ground truth, and the output of the network. The four represented images belong to different clusters.