Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks
In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same...
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Wiley
2020-01-01
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Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2020/6452536 |
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author | Linyan Li Yu Sun Fuyuan Hu Tao Zhou Xuefeng Xi Jinchang Ren |
author_facet | Linyan Li Yu Sun Fuyuan Hu Tao Zhou Xuefeng Xi Jinchang Ren |
author_sort | Linyan Li |
collection | DOAJ |
description | In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image. |
format | Article |
id | doaj-art-8a46cfd3ab634c6680d2d6eda5f78505 |
institution | Kabale University |
issn | 1026-0226 1607-887X |
language | English |
publishDate | 2020-01-01 |
publisher | Wiley |
record_format | Article |
series | Discrete Dynamics in Nature and Society |
spelling | doaj-art-8a46cfd3ab634c6680d2d6eda5f785052025-02-03T06:43:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/64525366452536Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial NetworksLinyan Li0Yu Sun1Fuyuan Hu2Tao Zhou3Xuefeng Xi4Jinchang Ren5College of Information Technology, Suzhou Institute of Trade & Commerce, Suzhou 215009, ChinaCollege of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaCollege of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaCollege of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaUniversity of Strathclyde, Glasgow, UKIn this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image.http://dx.doi.org/10.1155/2020/6452536 |
spellingShingle | Linyan Li Yu Sun Fuyuan Hu Tao Zhou Xuefeng Xi Jinchang Ren Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks Discrete Dynamics in Nature and Society |
title | Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks |
title_full | Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks |
title_fullStr | Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks |
title_full_unstemmed | Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks |
title_short | Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks |
title_sort | text to realistic image generation with attentional concatenation generative adversarial networks |
url | http://dx.doi.org/10.1155/2020/6452536 |
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