000099694 001__ 99694
000099694 005__ 20230519145419.0
000099694 0247_ $$2doi$$a10.1186/s40537-021-00414-0
000099694 0248_ $$2sideral$$a123134
000099694 037__ $$aART-2021-123134
000099694 041__ $$aeng
000099694 100__ $$aSampath, V.
000099694 245__ $$aA survey on generative adversarial networks for imbalance problems in computer vision tasks
000099694 260__ $$c2021
000099694 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099694 5203_ $$aAny computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.
000099694 536__ $$9info:eu-repo/grantAgreement/EC/H2020/814225/EU/DIGItal MANufacturing Technologies for Zero-defect Industry 4.0 Production/DIGIMAN4.0$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 814225-DIGIMAN4.0
000099694 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099694 590__ $$a10.835$$b2021
000099694 592__ $$a2.592$$b2021
000099694 594__ $$a14.4$$b2021
000099694 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b6 / 111 = 0.054$$c2021$$dQ1$$eT1
000099694 593__ $$aComputer Networks and Communications$$c2021$$dQ1
000099694 593__ $$aInformation Systems and Management$$c2021$$dQ1
000099694 593__ $$aHardware and Architecture$$c2021$$dQ1
000099694 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099694 700__ $$aMaurtua, I.
000099694 700__ $$0(orcid)0000-0002-8609-1358$$aAguilar Martín, J.J.$$uUniversidad de Zaragoza
000099694 700__ $$aGutierrez, A.
000099694 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000099694 773__ $$g8, 1 (2021), 27 [59 pp]$$tJournal of Big Data$$x2196-1115
000099694 8564_ $$s2718341$$uhttps://zaguan.unizar.es/record/99694/files/texto_completo.pdf$$yVersión publicada
000099694 8564_ $$s2255093$$uhttps://zaguan.unizar.es/record/99694/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099694 909CO $$ooai:zaguan.unizar.es:99694$$particulos$$pdriver
000099694 951__ $$a2023-05-18-14:04:51
000099694 980__ $$aARTICLE