Subject: self-supervised learning; transfer learning; image colorization; convolutional neural network; generative adversarial network
Year: 2022
Type: Article
Title: GAN-Based Image Colorization for Self-Supervised Visual Feature Learning
Author: Gievska, Sonja
Author: Treneska, Sandra
Author: Zdravevski, Eftim
Author: Pires, Ivan Miguel
Author: Lameski, Petre
Abstract: Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised learning methods have been proposed to learn general visual features automatically. In this paper, we first focus on image colorization with generative adversarial networks (GANs) because of their ability to generate the most realistic colorization results. Then, via transfer learning, we use this as a proxy task for visual understanding. Particularly, we propose to use conditional GANs (cGANs) for image colorization and transfer the gained knowledge to two other downstream tasks, namely, multilabel image classification and semantic segmentation. This is the first time that GANs have been used for self-supervised feature learning through image colorization. Through extensive experiments with the COCO and Pascal datasets, we show an increase of 5% for the classification task and 2.5% for the segmentation task. This demonstrates that image colorization with conditional GANs can boost other downstream tasks’ performance without the need for manual annotation.
Publisher: MDPI
Relation: Sensors
Identifier: oai:repository.ukim.mk:20.500.12188/20056
Identifier: http://hdl.handle.net/20.500.12188/20056
