The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Perkembangan terbaru model generatif berasaskan pembelajaran mendalam telah meningkatkan minat dalam sintesis data dan aplikasinya dengan ketara. Sintesis data mengambil kepentingan tambahan terutamanya untuk beberapa tugas pengecaman corak di mana beberapa kelas data jarang dan sukar untuk dikumpulkan. Dalam set data iris, contohnya, sampel kelas minoriti termasuk imej mata dengan cermin mata, murid bersaiz besar atau kecil, lokasi iris tidak sejajar dan iris tersumbat atau tercemar oleh kelopak mata, bulu mata atau pantulan pencahayaan. Set data tidak seimbang kelas sedemikian sering mengakibatkan prestasi pengelasan berat sebelah. Rangkaian musuh generatif (GAN) ialah salah satu rangka kerja yang paling menjanjikan yang belajar menjana data sintetik melalui permainan minimax dua pemain antara penjana dan diskriminator. Dalam makalah ini, kami menggunakan rangkaian musuh generatif Wasserstein bersyarat terkini dengan penalti kecerunan (CWGAN-GP) untuk menjana kelas minoriti imej iris yang menjimatkan sejumlah besar kos tenaga manusia untuk pengumpulan data yang jarang berlaku. Dengan model kami, penyelidik boleh menjana seberapa banyak imej iris bagi kes yang jarang berlaku seperti yang mereka mahu dan ia membantu membangunkan sebarang algoritma pembelajaran mendalam apabila set data saiz besar diperlukan.
Yung-Hui LI
National Central University,Hon Hai Research Institute
Muhammad Saqlain ASLAM
National Central University
Latifa Nabila HARFIYA
National Central University
Ching-Chun CHANG
University of Warwick
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Yung-Hui LI, Muhammad Saqlain ASLAM, Latifa Nabila HARFIYA, Ching-Chun CHANG, "Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 9, pp. 1450-1458, September 2021, doi: 10.1587/transinf.2021EDP7079.
Abstract: The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7079/_p
Salinan
@ARTICLE{e104-d_9_1450,
author={Yung-Hui LI, Muhammad Saqlain ASLAM, Latifa Nabila HARFIYA, Ching-Chun CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets},
year={2021},
volume={E104-D},
number={9},
pages={1450-1458},
abstract={The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.},
keywords={},
doi={10.1587/transinf.2021EDP7079},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets
T2 - IEICE TRANSACTIONS on Information
SP - 1450
EP - 1458
AU - Yung-Hui LI
AU - Muhammad Saqlain ASLAM
AU - Latifa Nabila HARFIYA
AU - Ching-Chun CHANG
PY - 2021
DO - 10.1587/transinf.2021EDP7079
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2021
AB - The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.
ER -