Relevant bibliographies by topics / Multi-domain image translation

Academic literature on the topic 'Multi-domain image translation'

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Published: 11 March 2023

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Journal articles on the topic "Multi-domain image translation"

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Shao, Mingwen, Youcai Zhang, Huan Liu, Chao Wang, Le Li, and Xun Shao. "DMDIT: Diverse multi-domain image-to-image translation." Knowledge-Based Systems 229 (October 2021): 107311. http://dx.doi.org/10.1016/j.knosys.2021.107311.

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Liu, Huajun, Lei Chen, Haigang Sui, Qing Zhu, Dian Lei, and Shubo Liu. "Unsupervised multi-domain image translation with domain representation learning." Signal Processing: Image Communication 99 (November 2021): 116452. http://dx.doi.org/10.1016/j.image.2021.116452.

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Cai, Naxin, Houjin Chen, Yanfeng Li, Yahui Peng, and Linqiang Guo. "Registration on DCE-MRI images via multi-domain image-to-image translation." Computerized Medical Imaging and Graphics 104 (March 2023): 102169. http://dx.doi.org/10.1016/j.compmedimag.2022.102169.

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Xia, Weihao, Yujiu Yang, and Jing-Hao Xue. "Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement." Neural Networks 131 (November 2020): 50–63. http://dx.doi.org/10.1016/j.neunet.2020.07.023.

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Shen, Yangyun, Runnan Huang, and Wenkai Huang. "GD-StarGAN: Multi-domain image-to-image translation in garment design." PLOS ONE 15, no. 4 (April 21, 2020): e0231719. http://dx.doi.org/10.1371/journal.pone.0231719.

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Zhang, Yifei, Weipeng Li, Daling Wang, and Shi Feng. "Unsupervised Image Translation Using Multi-Scale Residual GAN." Mathematics 10, no. 22 (November 19, 2022): 4347. http://dx.doi.org/10.3390/math10224347.

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Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversarial Network (MRGAN) based on unsupervised learning is proposed in this paper for transforming images between different domains using unpaired data. In the model, a dual generater architecture is used to eliminate the dependence on paired training samples and introduce a multi-scale layered residual network in generators for reducing semantic loss of images in the process of encoding. The Wasserstein GAN architecture with gradient penalty (WGAN-GP) is employed in the discriminator to optimize the training process and speed up the network convergence. Comparative experiments on several image translation tasks over style transfers and object migrations show that the proposed MRGAN outperforms strong baseline models by large margins.
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Xu, Wenju, and 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.

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Komatsu, Rina, and Tad Gonsalves. "Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation." AI 3, no. 1 (January 24, 2022): 37–52. http://dx.doi.org/10.3390/ai3010003.

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In CycleGAN, an image-to-image translation architecture was established without the use of paired datasets by employing both adversarial and cycle consistency loss. The success of CycleGAN was followed by numerous studies that proposed new translation models. For example, StarGAN works as a multi-domain translation model based on a single generator–discriminator pair, while U-GAT-IT aims to close the large face-to-anime translation gap by adapting its original normalization to the process. However, constructing robust and conditional translation models requires tradeoffs when the computational costs of training on graphic processing units (GPUs) are considered. This is because, if designers attempt to implement conditional models with complex convolutional neural network (CNN) layers and normalization functions, the GPUs will need to secure large amounts of memory when the model begins training. This study aims to resolve this tradeoff issue via the development of Multi-CartoonGAN, which is an improved CartoonGAN architecture that can output conditional translated images and adapt to large feature gap translations between the source and target domains. To accomplish this, Multi-CartoonGAN reduces the computational cost by using a pretrained VGGNet to calculate the consistency loss instead of reusing the generator. Additionally, we report on the development of the conditional adaptive layer-instance normalization (CAdaLIN) process for use with our model to make it robust to unique feature translations. We performed extensive experiments using Multi-CartoonGAN to translate real-world face images into three different artistic styles: portrait, anime, and caricature. An analysis of the visualized translated images and GPU computation comparison shows that our model is capable of performing translations with unique style features that follow the conditional inputs and at a reduced GPU computational cost during training.
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Feng, Long, Guohua Geng, Qihang Li, Yi Jiang, Zhan Li, and Kang Li. "CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy." PLOS ONE 18, no. 1 (January 6, 2023): e0280073. http://dx.doi.org/10.1371/journal.pone.0280073.

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Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generated images from global structures to local details by adding new convolution blocks continuously and shares the network parameters in different scales and also in the same scale of network. Finally, we propose a new Cross-CBAM mechanism (CRCBAM), which uses a multi-layer spatial attention and channel attention cross structure to generate more refined style images. Experiments on our collected Opera Face, and other open datasets Summer↔Winter, Horse↔Zebra, Photo↔Van Gogh, show that the proposed algorithm is superior to other state-of-art algorithms.
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Tao, Rentuo, Ziqiang Li, Renshuai Tao, and 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.

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Dissertations / Theses on the topic "Multi-domain image translation"

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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.

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Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of-the-art UNIT methods are either built from several two-domain mappings that are required to be learned independently or they generate low-diversity results, a phenomenon also known as model collapse. (2) Most of the manipulation is with the assistance of visual maps or digital labels without exploring natural languages, which could be more scalable and flexible in practice. (3) In an MMUIT system, the style latent space is usually disentangled between every two image domains. While interpolations within domains are smooth, interpolations between two different domains often result in unrealistic images with artifacts when interpolating between two randomly sampled style representations from two different domains. Improving the smoothness of the style latent space can lead to gradual interpolations between any two style latent representations even between any two domains. (4) It is expensive to train MMUIT models from scratch at high resolution. Interpreting the latent space of pre-trained unconditional GANs can achieve pretty good image translations, especially high-quality synthesized images (e.g., 1024x1024 resolution). However, few works explore building an MMUIT system with such pre-trained GANs. In this thesis, we focus on these vital issues and propose several techniques for building better MMUIT systems. First, we base on the content-style disentangled framework and propose to fit the style latent space with Gaussian Mixture Models (GMMs). It allows a well-trained network using a shared disentangled style latent space to model multi-domain translations. Meanwhile, we can randomly sample different style representations from a Gaussian component or use a reference image for style transfer. Second, we show how the GMM-modeled latent style space can be combined with a language model (e.g., a simple LSTM network) to manipulate multiple styles by using textual commands. Then, we not only propose easy-to-use constraints to improve the smoothness of the style latent space in MMUIT models, but also design a novel metric to quantitatively evaluate the smoothness of the style latent space. Finally, we build a new model to use pretrained unconditional GANs to do MMUIT tasks.
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Wu, Po-Wui, and 吳柏威. "RA-GAN: Multi-domain Image-to-Image Translation via Relative Attributes." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6q5k8e.

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碩士
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.
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Hsu, Shu-Yu, and 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.

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碩士
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.
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Yung-YuChang and 張詠裕. "Multi-Domain Image-to-Image Translations based on Generative Adversarial Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/89654d.

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碩士
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.
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Book chapters on the topic "Multi-domain image translation"

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He, Ziliang, Zhenguo Yang, Xudong Mao, Jianming Lv, Qing Li, and Wenyin Liu. "Self-attention StarGAN for Multi-domain Image-to-Image Translation." In Lecture Notes in Computer Science, 537–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30508-6_43.

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Cao, Jie, Huaibo Huang, Yi Li, Ran He, and Zhenan Sun. "Informative Sample Mining Network for Multi-domain Image-to-Image Translation." In Computer Vision – ECCV 2020, 404–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58529-7_24.

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Pan, Bing, Zexuan Ji, and Qiang Chen. "MultiGAN: Multi-domain Image Translation from OCT to OCTA." In Pattern Recognition and Computer Vision, 336–47. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_28.

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Tang, Hao, Dan Xu, Wei Wang, Yan Yan, and Nicu Sebe. "Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation." In Computer Vision – ACCV 2018, 3–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_1.

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Hsu, Shu-Yu, Chih-Yuan Yang, Chi-Chia Huang, and Jane Yung-jen Hsu. "SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation." In Computer Vision – ACCV 2018, 338–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20870-7_21.

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Luo, Lei, and William H. Hsu. "AMMUNIT: An Attention-Based Multimodal Multi-domain UNsupervised Image-to-Image Translation Framework." In Lecture Notes in Computer Science, 358–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_30.

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Ge, Hongwei, Yao Yao, Zheng Chen, and Liang Sun. "Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Multi-domain Image Translation." In Computer Vision – ACCV 2018, 398–413. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20890-5_26.

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Conference papers on the topic "Multi-domain image translation"

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Lin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin, and Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization." In 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.

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Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.
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Zhu, Yuanlue, Mengchao Bai, Linlin Shen, and Zhiwei Wen. "SwitchGAN for Multi-domain Facial Image Translation." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00209.

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Gomez, Raul, Yahui Liu, Marco De Nadai, Dimosthenis Karatzas, Bruno Lepri, and Nicu Sebe. "Retrieval Guided Unsupervised Multi-domain Image to Image Translation." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413785.

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Ge, Yingjun, Xiaodong Wang, and Jiting Zhou. "Federated learning based multi-domain image-to-image translation." In International Conference on Mechanisms and Robotics (ICMAR 2022), edited by Zeguang Pei. SPIE, 2022. http://dx.doi.org/10.1117/12.2652535.

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Hui, Le, Xiang Li, Jiaxin Chen, Hongliang He, and Jian Yang. "Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545169.

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Rahman, Mohammad Mahfujur, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan. "Multi-Component Image Translation for Deep Domain Generalization." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00067.

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Lin, Yu-Jing, Po-Wei Wu, Che-Han Chang, Edward Chang, and Shih-Wei Liao. "RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00601.

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Fu, Huiyuan, Ting Yu, Xin Wang, and Huadong Ma. "Cross-Granularity Learning for Multi-Domain Image-to-Image Translation." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413656.

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Nguyen, The-Phuc, Stephane Lathuiliere, and Elisa Ricci. "Multi-Domain Image-to-Image Translation with Adaptive Inference Graph." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412713.

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Yang, Xuewen, Dongliang Xie, and Xin Wang. "Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation." In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240716.

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