Relevant bibliographies by topics / Image-to-image translation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image-to-image translation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Image-to-image translation"

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Xu, Shuzhen, Qing Zhu, and Jin Wang. "Generative image completion with image-to-image translation." Neural Computing and Applications 32, no. 11 (2019): 7333–45. http://dx.doi.org/10.1007/s00521-019-04253-2.

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Xu, Shuzhen, Qing Zhu, and Jin Wang. "Correction to: Generative image completion with image-to-image translation." Neural Computing and Applications 32, no. 23 (2020): 17809. http://dx.doi.org/10.1007/s00521-020-05213-x.

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Cho, Younggun, Hyesu Jang, Ramavtar Malav, Gaurav Pandey, and Ayoung Kim. "Underwater Image Dehazing via Unpaired Image-to-image Translation." International Journal of Control, Automation and Systems 18, no. 3 (2020): 605–14. http://dx.doi.org/10.1007/s12555-019-0689-x.

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Yin, Xu, Yan Li, and Byeong-Seok Shin. "DAGAN: A Domain-Aware Method for Image-to-Image Translations." Complexity 2020 (March 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/9341907.

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The image-to-image translation method aims to learn inter-domain mappings from paired/unpaired data. Although this technique has been widely used for visual predication tasks—such as classification and image segmentation—and achieved great results, we still failed to perform flexible translations when attempting to learn different mappings, especially for images containing multiple instances. To tackle this problem, we propose a generative framework DAGAN (Domain-aware Generative Adversarial etwork) that enables domains to learn diverse mapping relationships. We assumed that an image is compos
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Kinakh, Vitaliy, Yury Belousov, Guillaume Quétant, et al. "Hubble Meets Webb: Image-to-Image Translation in Astronomy." Sensors 24, no. 4 (2024): 1151. http://dx.doi.org/10.3390/s24041151.

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This work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data. Comparative analysis encompasses the Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette methodologies, assessing the criticality of image registration in astronomy. While the focus of this study is not on the scientific evaluation of model fairness, we note that the techniques employed may bear some limitations and the translated images could include elements that are not present in actual astronomical phenomena. To mitigate this, unc
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Lin, Che-Tsung, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. "Multimodal Structure-Consistent Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11490–98. http://dx.doi.org/10.1609/aaai.v34i07.6814.

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Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimo
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Chen, Lei, Le Wu, Zhenzhen Hu, and Meng Wang. "Quality-Aware Unpaired Image-to-Image Translation." IEEE Transactions on Multimedia 21, no. 10 (2019): 2664–74. http://dx.doi.org/10.1109/tmm.2019.2907052.

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Hoyez, Henri, Cédric Schockaert, Jason Rambach, Bruno Mirbach, and Didier Stricker. "Unsupervised Image-to-Image Translation: A Review." Sensors 22, no. 21 (2022): 8540. http://dx.doi.org/10.3390/s22218540.

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Supervised image-to-image translation has been proven to generate realistic images with sharp details and to have good quantitative performance. Such methods are trained on a paired dataset, where an image from the source domain already has a corresponding translated image in the target domain. However, this paired dataset requirement imposes a huge practical constraint, requires domain knowledge or is even impossible to obtain in certain cases. Due to these problems, unsupervised image-to-image translation has been proposed, which does not require domain expertise and can take advantage of a
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Yin, Wenbin, Jun Yu, and Zhiyi Hu. "Generating Sea Surface Object Image Using Image-to-Image Translation." International Journal of Advanced Network, Monitoring and Controls 6, no. 2 (2021): 48–55. http://dx.doi.org/10.21307/ijanmc-2021-016.

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Zhao, Xi, Haizheng Yu, and Hong Bian. "Image to Image Translation Based on Differential Image Pix2Pix Model." Computers, Materials & Continua 77, no. 1 (2023): 181–98. http://dx.doi.org/10.32604/cmc.2023.041479.

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Dissertations / Theses on the topic "Image-to-image translation"

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Ackerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.

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We expand the scope of image-to-image translation to include more distinct image domains, where the image sets have analogous structures, but may not share object types between them. Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains (SUNIT) is built to more successfully translate images in this setting, where content from one domain is not found in the other. Our method trains an image translation model by learning encodings for semantic segmentations of images. These segmentations are translated between image domains to learn meaningful mappings between the st
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Zhu, Anqing. "Translation From Image to Building." Thesis, KTH, Arkitektur, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-116871.

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After the fire accident, KTH school of architecture is no longer in its best performance. The school indeed need an extension. However, through out this thesis project, I attempt to take one step further. It is not only to construct a functional school, but reconsider the meaning of architectural education. I am interested in three aspects, which all of them has being driving forces to this project. Firstly, I was interested in memory of the old architecture school in Stockholm, which was accommodated in a 19th centry wooden building. It was old and small, but students loved it. One’s memory o
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Bujwid, Sebastian. "GANtruth – a regularization method for unsupervised image-to-image translation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233849.

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In this work, we propose a novel and effective method for constraining the output space of the ill-posed problem of unsupervised image-to-image translation. We make the assumption that the environment of the source domain is known, and we propose to explicitly enforce preservation of the ground-truth labels on the images translated from the source to the target domain. We run empirical experiments on preserving information such as semantic segmentation and disparity and show evidence that our method achieves improved performance over the baseline model UNIT on translating images from SYNTHIA t
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Sveding, Jens Jakob. "Unsupervised Image-to-image translation : Taking inspiration from human perception." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105500.

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Generative Artificial Intelligence is a field of artificial intelligence where systems can learn underlying patterns in previously seen content and generate new content. This thesis explores a generative artificial intelligence technique used for image-toimage translations called Cycle-consistent Adversarial network (CycleGAN), which can translate images from one domain into another. The CycleGAN is a stateof-the-art technique for doing unsupervised image-to-image translations. It uses the concept of cycle-consistency to learn a mapping between image distributions, where the Mean Absolute Erro
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Pizzati, Fabio <1993&gt. "Exploring domain-informed and physics-guided learning in image-to-image translation." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10499/1/pizzati_fabio_tesi.pdf.

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Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged t
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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
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Karlsson, Simon, and Per Welander. "Generative Adversarial Networks for Image-to-Image Translation on Street View and MR Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148475.

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Generative Adversarial Networks (GANs) is a deep learning method that has been developed for synthesizing data. One application for which it can be used for is image-to-image translations. This could prove to be valuable when training deep neural networks for image classification tasks. Two areas where deep learning methods are used are automotive vision systems and medical imaging. Automotive vision systems are expected to handle a broad range of scenarios which demand training data with a high diversity. The scenarios in the medical field are fewer but the problem is instead that it is diffi
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Müller, Markus [Verfasser], and B. [Akademischer Betreuer] Jutzi. "Camera Re-Localization with Data Augmentation by Image Rendering and Image-to-Image Translation / Markus Müller ; Betreuer: B. Jutzi." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1209199106/34.

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Hamrell, Hanna. "Image-to-Image Translation for Improvement of Synthetic Thermal Infrared Training Data Using Generative Adversarial Networks." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174928.

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Training data is an essential ingredient within supervised learning, yet time con-suming, expensive and for some applications impossible to retrieve. Thus it isof interest to use synthetic training data. However, the domain shift of syntheticdata makes it challenging to obtain good results when used as training data fordeep learning models. It is therefore of interest to refine synthetic data, e.g. using image-to-image translation, to improve results. The aim of this work is to compare different methods to do image-to-image translation of synthetic training data of thermal IR-images using GANs
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Tang, Hao. "Learning to Generate Things and Stuff: Guided Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/306790.

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In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image
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Books on the topic "Image-to-image translation"

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Crombie, John. The ill-made image: Being a critique of the unacknowledged English translation of Samuel Beckett's L'image, published by John Calder under the title "The image" in As the story was told, in anticipation of, and as a contribution to, any revised edition authorized by the literary executor or heirs to the Beckett estate. [The Author], 1995.

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1948-, Sinkewicz Robert E., and Pontifical Institute of Mediaeval Studies, eds. Saint Gregory Palamas: The one hundred and fifty chapters, a critical edition, translation and study. Pontifical Institute of Mediaeval Studies, 1988.

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Rao, K. Sreenivasa. Predicting Prosody from Text for Text-to-Speech Synthesis. Springer New York, 2012.

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J, Sahas Daniel, ed. Icon and logos: Sources in eighth-century iconoclasm : an annotated translation of the sixth session of the seventh Ecumenical Council (Nicea, 787) ... University of Toronto Press, 1986.

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Solanki, Arun, Anand Nayyar, and Mohd Naved. Generative Adversarial Networks for Image-To-Image Translation. Elsevier Science & Technology Books, 2021.

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Generative Adversarial Networks for Image-to-Image Translation. Elsevier, 2021. http://dx.doi.org/10.1016/c2020-0-00284-7.

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Solanki, Arun, Anand Nayyar, and Mohd Naved. Generative Adversarial Networks for Image-To-Image Translation. Elsevier Science & Technology, 2021.

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C[underscore]Images image analysis and image processing library: Version 5.2. : Library reference functions A-E [and]A programmer's guide to C[underscore]Images for data translation DT 2855 board. Foster Findlay Associates, 1994.

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Vernallis, Carol, Amy Herzog, and John Richardson, eds. The Oxford Handbook of Sound and Image in Digital Media. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199757640.001.0001.

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This collection of essays explores the relations between sound and image in a rapidly shifting landscape of audiovisual media in the digital age. Featuring contributions from scholars who bring with them an impressive array of disciplinary expertise, from film studies and philosophy to musicology, pornography, digital gaming, and media studies, the book charts new territory by analyzing what it calls the “media swirl” and the “audiovisual turn.” It draws on a range of media texts including blockbuster cinema, video art, music videos, video games, amateur video compilations, visualization techn
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Wan Qing zhi wu si wen xue fan yi yu min zu xing xiang gou jian: Literary translation as a means for constructing Chinese national image from late Qing dynasty to the May 4th movement. Jiu zhou chu ban she, 2020.

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Book chapters on the topic "Image-to-image translation"

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Chen, Yu-Jie, Shin-I. Cheng, Wei-Chen Chiu, Hung-Yu Tseng, and Hsin-Ying Lee. "Vector Quantized Image-to-Image Translation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19787-1_25.

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Huang, Xun, Ming-Yu Liu, Serge Belongie, and Jan Kautz. "Multimodal Unsupervised Image-to-Image Translation." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01219-9_11.

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Eusebio, Jose, Hemanth Venkateswara, and Sethuraman Panchanathan. "Semi-supervised Adversarial Image-to-Image Translation." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04375-9_28.

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Murez, Zak, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, and Kyungnam Kim. "Domain Adaptation via Image to Image Translation." In Domain Adaptation in Computer Vision with Deep Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_7.

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Shahfar, Shima, and Charalambos Poullis. "Unsupervised Structure-Consistent Image-to-Image Translation." In Advances in Visual Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20713-6_1.

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Kumari, Rashmi, Subhranil Das, and Raghwendra Kishore Singh. "Generative Models for Image-to-Image Translation." In Generative Intelligence in Healthcare. CRC Press, 2025. https://doi.org/10.1201/9781003539483-2.

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Park, Taesung, Alexei A. Efros, Richard Zhang, and Jun-Yan Zhu. "Contrastive Learning for Unpaired Image-to-Image Translation." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58545-7_19.

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Zhao, Qianyi, Mengyin Wang, Qing Zhang, Fasheng Wang, and Fuming Sun. "OmniStyleGAN for Style-Guided Image-to-Image Translation." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8795-1_24.

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Desai, Digvijay, Shreyash Zanjal, Abhishek Kasar, Jayashri Bagade, and Yogesh Dandawate. "Image-to-Image Translation Using Generative Adversarial Networks." In Generative Adversarial Networks and Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003203964-2.

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Lai, Binxin, and Yuan-Gen Wang. "Unsupervised Image-to-Image Translation with Style Consistency." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8537-1_26.

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Conference papers on the topic "Image-to-image translation"

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Vats, Anuja, Ivar Farup, Marius Pedersen, and Kiran Raja. "Uncertainty-Aware Regularization for Image-to-Image Translation." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00390.

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Björnberg, Dag, Morgan Ericsson, Welf Löwe, and Jonas Nordqvist. "Unpaired Image-to-Image Translation to Improve Log End Identification." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-63.

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Zerari, Abd El Mouméne, Ayoub Guettal, Mohamed Nadiib Mead, and Mohamed Chaouki Babahenini. "Paired Image to Image Translation for Ambient Occlusion Approximation." In 2025 International Symposium on iNnovative Informatics of Biskra (ISNIB). IEEE, 2025. https://doi.org/10.1109/isnib64820.2025.10982756.

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Lin, Jianxin, Yingce Xia, Tao Qin, Zhibo Chen, and Tie-Yan Liu. "Conditional Image-to-Image Translation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00579.

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Grassucci, Eleonora, Luigi Sigillo, Aurelio Uncini, and Danilo Comminiello. "Hypercomplex Image- to- Image Translation." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892119.

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Plabon, Silvia Satoar, Mohammad Shabaj Khan, Md Khaliluzzaman, and Md Rashedul Islam. "Image Translator: An Unsupervised Image-to-Image Translation Approach using GAN." In 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE, 2022. http://dx.doi.org/10.1109/iciset54810.2022.9775902.

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Chen, Ying-Cong, Xiaogang Xu, and Jiaya Jia. "Domain Adaptive Image-to-Image Translation." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00532.

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Basavaraju, Sathisha, Prasen Kumar Sharma, and Arijit Sur. "Memorability based image to image translation." In Twelfth International Conference on Machine Vision, edited by Wolfgang Osten and Dmitry P. Nikolaev. SPIE, 2020. http://dx.doi.org/10.1117/12.2556543.

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Oza, Manan, Himanshu Vaghela, and Sudhir Bagul. "Semi-Supervised Image-to-Image Translation." In 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT). IEEE, 2019. http://dx.doi.org/10.1109/icaiit.2019.8834613.

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Parmar, Gaurav, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. "Zero-shot Image-to-Image Translation." In SIGGRAPH '23: Special Interest Group on Computer Graphics and Interactive Techniques Conference. ACM, 2023. http://dx.doi.org/10.1145/3588432.3591513.

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Reports on the topic "Image-to-image translation"

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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glos
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