Using GANs to Augment Data for Cloud Image Segmentation Task

• URL: http://arxiv.org/abs/2106.03064v1
• Date: Sun, 6 Jun 2021 09:01:43 GMT
• Title: Using GANs to Augment Data for Cloud Image Segmentation Task
• Authors: Mayank Jain, Conor Meegan, and Soumyabrata Dev
• Abstract summary: We show the effectiveness of using Generative Adversarial Networks (GANs) to generate data to augment the training set. We also present a way to estimate ground-truth binary maps for the GAN-generated images to facilitate their effective use as augmented images.
• Score: 2.294014185517203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While cloud/sky image segmentation has extensive real-world applications, a large amount of labelled data is needed to train a highly accurate models to perform the task. Scarcity of such volumes of cloud/sky images with corresponding ground-truth binary maps makes it highly difficult to train such complex image segmentation models. In this paper, we demonstrate the effectiveness of using Generative Adversarial Networks (GANs) to generate data to augment the training set in order to increase the prediction accuracy of image segmentation model. We further present a way to estimate ground-truth binary maps for the GAN-generated images to facilitate their effective use as augmented images. Finally, we validate our work with different statistical techniques.

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