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 metalearning, 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.

MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization / Fontanini, Tomaso; Iotti, Eleonora; Prati, Andrea. - (2019), pp. 280-291. ((Intervento presentato al convegno International Conference on Image Analysis and Processing tenutosi a Trento, Italy nel 9-13 September 2019.

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

Tomaso Fontanini
Methodology
;
Eleonora Iotti
Software
;
Andrea Prati
Supervision
2019

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 metalearning, 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.
978-3-030-30642-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2863707
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