GAN-Based Image Colorization for Self-Supervised Visual Feature Learning
Journal
Sensors
Date Issued
2022-02-18
Author(s)
Treneska, Sandra
Pires, Ivan Miguel
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.
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.
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