GAN-Based Image Colorization for Self-Supervised Visual Feature Learning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 4; p. 1599
• Main Authors: Treneska, Sandra, Zdravevski, Eftim, Pires, Ivan Miguel, Lameski, Petre, Gievska, Sonja
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2022; MDPI
• Subjects: Classification; Colorization; Computer vision; Computers; convolutional neural network; Datasets; generative adversarial network; Generative adversarial networks; Human error; Image annotation; Image classification; image colorization; Image Processing, Computer-Assisted - methods; Knowledge management; Learning; Liquors; Machine vision; Medical imaging equipment; Neural networks; Neural Networks, Computer; self-supervised learning; Semantics; transfer learning; Vision, Ocular; Visual tasks; generative adversarial network; transfer learning; self-supervised learning; image colorization; convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22041599

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.