Literatura académica sobre el tema "Multi-domain image translation"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Multi-domain image translation".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Multi-domain image translation"
Shao, Mingwen, Youcai Zhang, Huan Liu, Chao Wang, Le Li y Xun Shao. "DMDIT: Diverse multi-domain image-to-image translation". Knowledge-Based Systems 229 (octubre de 2021): 107311. http://dx.doi.org/10.1016/j.knosys.2021.107311.
Texto completoLiu, Huajun, Lei Chen, Haigang Sui, Qing Zhu, Dian Lei y Shubo Liu. "Unsupervised multi-domain image translation with domain representation learning". Signal Processing: Image Communication 99 (noviembre de 2021): 116452. http://dx.doi.org/10.1016/j.image.2021.116452.
Texto completoCai, Naxin, Houjin Chen, Yanfeng Li, Yahui Peng y Linqiang Guo. "Registration on DCE-MRI images via multi-domain image-to-image translation". Computerized Medical Imaging and Graphics 104 (marzo de 2023): 102169. http://dx.doi.org/10.1016/j.compmedimag.2022.102169.
Texto completoXia, Weihao, Yujiu Yang y Jing-Hao Xue. "Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement". Neural Networks 131 (noviembre de 2020): 50–63. http://dx.doi.org/10.1016/j.neunet.2020.07.023.
Texto completoShen, Yangyun, Runnan Huang y Wenkai Huang. "GD-StarGAN: Multi-domain image-to-image translation in garment design". PLOS ONE 15, n.º 4 (21 de abril de 2020): e0231719. http://dx.doi.org/10.1371/journal.pone.0231719.
Texto completoZhang, Yifei, Weipeng Li, Daling Wang y Shi Feng. "Unsupervised Image Translation Using Multi-Scale Residual GAN". Mathematics 10, n.º 22 (19 de noviembre de 2022): 4347. http://dx.doi.org/10.3390/math10224347.
Texto completoXu, Wenju y Guanghui Wang. "A Domain Gap Aware Generative Adversarial Network for Multi-Domain Image Translation". IEEE Transactions on Image Processing 31 (2022): 72–84. http://dx.doi.org/10.1109/tip.2021.3125266.
Texto completoKomatsu, Rina y Tad Gonsalves. "Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation". AI 3, n.º 1 (24 de enero de 2022): 37–52. http://dx.doi.org/10.3390/ai3010003.
Texto completoFeng, Long, Guohua Geng, Qihang Li, Yi Jiang, Zhan Li y Kang Li. "CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy". PLOS ONE 18, n.º 1 (6 de enero de 2023): e0280073. http://dx.doi.org/10.1371/journal.pone.0280073.
Texto completoTao, Rentuo, Ziqiang Li, Renshuai Tao y Bin Li. "ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation". IEEE Access 7 (2019): 132594–608. http://dx.doi.org/10.1109/access.2019.2941272.
Texto completoTesis sobre el tema "Multi-domain image translation"
Liu, Yahui. "Exploring Multi-Domain and Multi-Modal Representations for Unsupervised Image-to-Image Translation". Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342634.
Texto completoWu, Po-Wui y 吳柏威. "RA-GAN: Multi-domain Image-to-Image Translation via Relative Attributes". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6q5k8e.
Texto completo國立臺灣大學
資訊工程學研究所
107
Multi-domain image-to-image translation has gained increasing attention recently. Previous methods take an image and some target attributes as inputs and generate an output image that has the desired attributes. However, this has one limitation. They require specifying the entire set of attributes even most of them would not be changed. To address this limitation, we propose RA-GAN, a novel and practical formulation to multi-domain image-to-image translation. The key idea is the use of relative attributes, which describes the desired change on selected attributes. To this end, we propose an adversarial framework that learns a single generator to translate images that not only match the relative attributes but also exhibit better quality. Moreover, Our generator is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other ones. Experimental results demonstrate the effectiveness of our approach both qualitatively and quantitatively to the tasks of facial attribute transfer and interpolation.
Hsu, Shu-Yu y 許書宇. "SemiStarGAN: Semi-Supervised Generative Adversarial Networks for Multi-Domain Image-to-Image Translation". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n4zqyy.
Texto completo國立臺灣大學
資訊工程學研究所
106
Recent studies have shown significant advance for multi-domain image-to-image translation, and generative adversarial networks (GANs) are widely used to address this problem. However, existing methods all require a large number of domain-labeled images to train an effective image generator, but it may take time and effort to collect a large number of labeled data for real-world problems. In this thesis, we propose SemiStarGAN, a semi-supervised GAN network to tackle this issue. The proposed method utilizes unlabeled images by incorporating a novel discriminator/classifier network architecture Y model, and two existing semi-supervised learning techniques---pseudo labeling and self-ensembling. Experimental results on the CelebA dataset using domains of facial attributes show that the proposed method achieves comparable performance with state-of-the-art methods using considerably less labeled training images.
Yung-YuChang y 張詠裕. "Multi-Domain Image-to-Image Translations based on Generative Adversarial Networks". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/89654d.
Texto completo國立成功大學
工程科學系
106
In recent years, domain translation has been a breakthrough in the field of deep learning. However, most of the issues raised so far are dedicated to a single situation, and trained through paired datasets. The effect is significant, but the defect is that the architectures lack scalability and the paired data update in the future is difficult. The demand for computer vision assistance systems is increasing, and there is more than one mission requirement in some environments. In this Thesis, we propose a multi-domain image translation model which has two advantages in terms of flexibility: one is the depth of the architecture that can be designed according to expectations, and the other is the number of domains that can be designed according to the number of tasks. We demonstrate the effectiveness of our theory on dehaze, debluring, and denoising tasks.
Capítulos de libros sobre el tema "Multi-domain image translation"
He, Ziliang, Zhenguo Yang, Xudong Mao, Jianming Lv, Qing Li y Wenyin Liu. "Self-attention StarGAN for Multi-domain Image-to-Image Translation". En Lecture Notes in Computer Science, 537–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30508-6_43.
Texto completoCao, Jie, Huaibo Huang, Yi Li, Ran He y Zhenan Sun. "Informative Sample Mining Network for Multi-domain Image-to-Image Translation". En Computer Vision – ECCV 2020, 404–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58529-7_24.
Texto completoPan, Bing, Zexuan Ji y Qiang Chen. "MultiGAN: Multi-domain Image Translation from OCT to OCTA". En Pattern Recognition and Computer Vision, 336–47. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_28.
Texto completoTang, Hao, Dan Xu, Wei Wang, Yan Yan y Nicu Sebe. "Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation". En Computer Vision – ACCV 2018, 3–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_1.
Texto completoHsu, Shu-Yu, Chih-Yuan Yang, Chi-Chia Huang y Jane Yung-jen Hsu. "SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation". En Computer Vision – ACCV 2018, 338–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20870-7_21.
Texto completoLuo, Lei y William H. Hsu. "AMMUNIT: An Attention-Based Multimodal Multi-domain UNsupervised Image-to-Image Translation Framework". En Lecture Notes in Computer Science, 358–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_30.
Texto completoGe, Hongwei, Yao Yao, Zheng Chen y Liang Sun. "Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Multi-domain Image Translation". En Computer Vision – ACCV 2018, 398–413. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20890-5_26.
Texto completoActas de conferencias sobre el tema "Multi-domain image translation"
Lin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin y Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/413.
Texto completoZhu, Yuanlue, Mengchao Bai, Linlin Shen y Zhiwei Wen. "SwitchGAN for Multi-domain Facial Image Translation". En 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00209.
Texto completoGomez, Raul, Yahui Liu, Marco De Nadai, Dimosthenis Karatzas, Bruno Lepri y Nicu Sebe. "Retrieval Guided Unsupervised Multi-domain Image to Image Translation". En MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413785.
Texto completoGe, Yingjun, Xiaodong Wang y Jiting Zhou. "Federated learning based multi-domain image-to-image translation". En International Conference on Mechanisms and Robotics (ICMAR 2022), editado por Zeguang Pei. SPIE, 2022. http://dx.doi.org/10.1117/12.2652535.
Texto completoHui, Le, Xiang Li, Jiaxin Chen, Hongliang He y Jian Yang. "Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders". En 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545169.
Texto completoRahman, Mohammad Mahfujur, Clinton Fookes, Mahsa Baktashmotlagh y Sridha Sridharan. "Multi-Component Image Translation for Deep Domain Generalization". En 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00067.
Texto completoLin, Yu-Jing, Po-Wei Wu, Che-Han Chang, Edward Chang y Shih-Wei Liao. "RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes". En 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00601.
Texto completoFu, Huiyuan, Ting Yu, Xin Wang y Huadong Ma. "Cross-Granularity Learning for Multi-Domain Image-to-Image Translation". En MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413656.
Texto completoNguyen, The-Phuc, Stephane Lathuiliere y Elisa Ricci. "Multi-Domain Image-to-Image Translation with Adaptive Inference Graph". En 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412713.
Texto completoYang, Xuewen, Dongliang Xie y Xin Wang. "Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation". En MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240716.
Texto completo