MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization

TL;DR

This paper introduces MetalGAN, a novel approach combining adversarial training and meta-learning with dataset clustering to achieve high-quality image colorization even with limited data.

Contribution

It presents a new cluster-based adaptive training method that enhances few-shot image colorization by integrating meta-learning with adversarial techniques.

Findings

Effective colorization with scarce data

Improved training efficiency through dataset clustering

Potential for high-quality results in low-data scenarios

Abstract

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.