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

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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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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Sepasdar, Reza. "A Deep Learning Approach to Predict Full-Field Stress Distribution in Composite Materials." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103427.

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This thesis proposes a deep learning approach to predict stress at various stages of mechanical loading in 2-D representations of fiber-reinforced composites. More specifically, the full-field stress distribution at elastic and at an early stage of damage initiation is predicted based on the microstructural geometry. The required data set for the purposes of training and validation are generated via high-fidelity simulations of several randomly generated microstructural representations with complex geometries. Two deep learning approaches are employed and their performances are compared: fully

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Xu, Libo. "GAN-based Automatic Segmentation of Thoracic Aorta from Non-contrast-Enhanced CT Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295428.

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The deep learning-based automatic segmentation methods have developed rapidly in recent years to give a promising performance in the medical image segmentation tasks, which provide clinical medicine with an accurate and fast computer-aided diagnosis method. Generative adversarial networks and their extended frameworks have achieved encouraging results on image-to-image translation problems. In this report, the proposed hybrid network combined cycle-consistent adversarial networks, which transformed contrast-enhanced images from computed tomography angiography to the conventional low-contrast C

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Damaskinidis, George. "Image analysis for translating English multimodal texts into Greek : a multimodal semiotics approach to translation training in a Greek higher education context." Thesis, Open University, 2012. http://oro.open.ac.uk/49080/.

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This study explores the image analysis practices of undergraduate translation trainees in a Greek. university. The key research questions focused on the trainees' perception of the role of visual literacy in translation, the nature of their verbal and visual associations and activities effective for developing this role. Visual literacy, semiotics, and translation theories provided the conceptual framework for this multimodal semiotics approach to translation training, which guided my examination of the way the trainees described semiotic resources, how they used verbal and visual elements of

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Graffieti, Gabriele. "Style Transfer with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17015/.

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This dissertation is focused on trying to use concepts from style transfer and image-to-image translation to address the problem of defogging. Defogging (or dehazing) is the ability to remove fog from an image, restoring it as if the photograph was taken during optimal weather conditions. The task of defogging is of particular interest in many fields, such as surveillance or self driving cars. In this thesis an unpaired approach to defogging is adopted, trying to translate a foggy image to the correspondent clear picture without having pairs of foggy and ground truth haze-free images during

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16

Moniruzzaman, Md. "Teleoperation enhancement for improved control of unmanned ground vehicles through video transformation and deep learning based future frame prediction." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2023. https://ro.ecu.edu.au/theses/2644.

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In this era of the fourth industrial revolution, the advancement of computer systems, artificial intelligence, computer vision, and automation are playing increasingly significant roles. The rapid levels of advancement in these technologies are making robotics an integral part of many aspects of human venture. More specifically, for applications such as item delivery, telesurgery, industrial production, search and rescue, mining, underwater, aerial and space exploration, and defence, robotic systems are becoming increasingly capable. Although the expectation is that such robotic systems will b

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Yeung, Karen. "Music translation an exploration of how sound and image work in a moving-image sequence : this exgesis is submitted to Auckland University of Technology for the degree of Bachelor of Art & Design (Honours), Oct. 2005 /." Full dissertation Abstract, 2005.

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Thesis (BA (Hons)--Art and Design) -- AUT University, 2005.<br>Print copy accompanied by CD. Includes bibliographical references. Also held in print (iii, 26 leaves : ill. ; 30 cm. + CD (4 3/4)) in City Campus Theses Collection (T 776.7 YEU)

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18

Chahal, Badaoui. "A translation into arabic of Ronald Sutherland's second image: comparative studies in Québec/Canadian literature with an introduction to the text comprising linguistic and cultural parallels and differences between Canada and the Arab world that bear directly on this translation." Thèse, Université de Sherbrooke, 1987. http://hdl.handle.net/11143/10084.

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My dissertation aims at contributing in an immediately practical fashion to a trans-cultural dialogue and broader understanding between Canada and the Arab world. The trans-lation of Ronald Sutherland's Second Image: Comparative Studies in Quebec/Canadian Literature will fan an already lively interest in Canadian and Australian literatures, as well as the literature produced by the third world coun-tries, in Lebanon and other countries of the Arab world. There are parallels in the political and cultural tensions in Canada and in Lebanon, which are reflected in the litera-tures of the two coun

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MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classiﬁcation of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the

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Maragos, Petros A. "A unified theory of translation-invariant systems with applications to morphological analysis and coding of images." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/14833.

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21

Ye, Mao. "Evaluating English translations of ancient Chinese poetry with special reference to image schemas and foregrounding." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/27839/.

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Poetry translation evaluation from ancient Chinese to English has been subjective in China. This is caused by the indefinable and intangible notion of ‘poetic spirit’, which is often used in influential translators’ criteria, and by the lack of a systematic investigation of translation evaluation. The problem of subjective criteria has remained unresolved for nearly a century. In order to improve the subjective criteria of poetry translation evaluation, this thesis is an attempt to make objective evaluations of the English translations of an ancient Chinese poem using stylistic theories. To ma

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JAFARI, MUHAMMAD REZA. "PERSIAN SIGN GESTURE TRANSLATION TO ENGLISH SPOKEN LANGUAGE ON SMARTPHONE." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18787.

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Hearing impaired and others with verbal challenges face difficulty to communicate with society; Sign Language represents their communication such as numbers or phrases. The communication becomes a challenge with people from other countries using different languages. Additionally, the sign language is different from one country to another. That is, learning one sign language doesn’t mean learning all sign languages. To translate a word from sign language to a spoken language is a challenge and to change a particular word from that language to another language is even a bigger challenge. I

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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<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>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 attrib

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Rao, Zhi-Hong, and 饒志鴻. "Study on Image to Image Translation Based on Cycle Consistent Adversarial Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dumkj5.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>106<br>With the development of machine learning related technologies in recent years, the concept of deep learning has been successfully applied to different fields, including image recognition, object detection, natural language processing, automatic generation of multimedia content, etc. The main idea is using deep neural network to find the hidden common feature in the large numbers of training data. Generative Adversarial Networks (GAN) is one of the most popular models in many deep neural network models and it can achieve amazing results when applied to cross-do

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Chang, Hsin-Yu, and 張心愉. "Unsupervised Content-Preserving Example-Guided Image-to-Image Translation via Domain Mapping." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/xz8e64.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>Unsupervised Image-to-Image across domains is an important problem in the field of computer vision. The most challenging task in this works is to preserve content information while performing style translation. We define what the content information we need to preserve in the cross domain translation and explore the trade-off between content preservation and style transfer. Current methods suffer from this trade-off and fail to strike a balance between content and style. For instance, the cat-to-dog translation task, the main problem is that there is a large

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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<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>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 discriminat

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"Multimodal Representation Learning for Visual Reasoning and Text-to-Image Translation." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.51644.

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abstract: Multimodal Representation Learning is a multi-disciplinary research field which aims to integrate information from multiple communicative modalities in a meaningful manner to help solve some downstream task. These modalities can be visual, acoustic, linguistic, haptic etc. The interpretation of ’meaningful integration of information from different modalities’ remains modality and task dependent. The downstream task can range from understanding one modality in the presence of information from other modalities, to that of translating input from one modality to another. In this thesis t

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Lavoie-Marchildon, Samuel. "Representation learning in unsupervised domain translation." Thesis, 2019. http://hdl.handle.net/1866/24324.

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Ce mémoire s'adresse au problème de traduction de domaine non-supervisée. La traduction non-supervisée cherche à traduire un domaine, le domaine source, à un domaine cible sans supervision. Nous étudions d'abord le problème en utilisant le formalisme du transport optimal. Dans un second temps, nous étudions le problème de transfert de sémantique à haut niveau dans les images en utilisant les avancés en apprentissage de représentations et de transfert d'apprentissages développés dans la communauté d'apprentissage profond. Le premier chapitre est dévoué à couvrir les bases des concepts utilis

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Parracho, João Oliveira. "JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS." Master's thesis, 2020. http://hdl.handle.net/10400.8/6682.

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The massive volume of data generated daily by the gathering of medical images with different modalities might be difficult to store in medical facilities and share through communication networks. To alleviate this issue, efficient compression methods must be implemented to reduce the amount of storage and transmission resources required in such applications. However, since the preservation of all image details is highly important in the medical context, the use of lossless image compression algorithms is of utmost importance. This thesis presents the research results on a lossless compr

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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<br>國立成功大學<br>工程科學系<br>106<br>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 advanta

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Manion, Steve Lawrence. "Fluency enhancement : applications to machine translation : thesis for Master of Engineering in Information & Telecommunications Engineering, Massey University, Palmerston North, New Zealand." 2009. http://hdl.handle.net/10179/1237.

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The quality of Machine Translation (MT) can often be poor due to it appearing incoherent and lacking in fluency. These problems consist of word ordering, awkward use of words and grammar, and translating text too literally. However we should not consider translations such as these failures until we have done our best to enhance their quality, or more simply, their fluency. In the same way various processes can be applied to touch up a photograph, various processes can also be applied to touch up a translation. This research outlines the improvement of MT quality through the application of Flue

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