Relevant bibliographies by topics / GAN adaptation

Contents

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

Academic literature on the topic 'GAN adaptation'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "GAN adaptation"

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Feng, Chengjian, Zhaoshui He, Jiawei Wang, Qinzhuang Lin, Zhouping Zhu, Jun Lu, and Shengli Xie. "Domain adaptation with SBADA-GAN and Mean Teacher." Neurocomputing 396 (July 2020): 577–86. http://dx.doi.org/10.1016/j.neucom.2018.12.089.

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Sanabria, Andrea Rosales, Franco Zambonelli, and Juan Ye. "Unsupervised Domain Adaptation in Activity Recognition: A GAN-Based Approach." IEEE Access 9 (2021): 19421–38. http://dx.doi.org/10.1109/access.2021.3053704.

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Rahman, Aimon, M. Sohel Rahman, and M. R. C. Mahdy. "3C-GAN: class-consistent CycleGAN for malaria domain adaptation model." Biomedical Physics & Engineering Express 7, no. 5 (July 7, 2021): 055002. http://dx.doi.org/10.1088/2057-1976/ac0e74.

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Ismail, Fawad, Palash Sarker, Mohamed Mohamed, Kyekyoon Kim, and Umberto Ravaioli. "Moving mesh adaptation for Si and GaN-based power device simulation." Journal of Computational Electronics 17, no. 4 (July 24, 2018): 1621–29. http://dx.doi.org/10.1007/s10825-018-1218-5.

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Wang, Xiaoqing, Xiangjun Wang, and Yubo Ni. "Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks." Computational Intelligence and Neuroscience 2018 (July 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7208794.

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In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.
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Benjdira, Bilel, Adel Ammar, Anis Koubaa, and Kais Ouni. "Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks." Applied Sciences 10, no. 3 (February 6, 2020): 1092. http://dx.doi.org/10.3390/app10031092.

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Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.
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Sajun, Ali Reza, and Imran Zualkernan. "Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning." Applied Sciences 12, no. 3 (February 7, 2022): 1718. http://dx.doi.org/10.3390/app12031718.

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Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified.
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Yu, Wentao, Jing Bai, and Licheng Jiao. "Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos." IEEE Access 8 (2020): 119144–57. http://dx.doi.org/10.1109/access.2020.3004495.

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Hu, Wenpeng, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao, and Rui Yan. "Predictive Adversarial Learning from Positive and Unlabeled Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7806–14. http://dx.doi.org/10.1609/aaai.v35i9.16953.

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This paper studies learning from positive and unlabeled examples, known as PU learning. It proposes a novel PU learning method called Predictive Adversarial Networks (PAN) based on GAN (Generative Adversarial Networks). GAN learns a generator to generate data (e.g., images) to fool a discriminator which tries to determine whether the generated data belong to a (positive) training class. PU learning can be casted as trying to identify (not generate) likely positive instances from the unlabeled set to fool a discriminator that determines whether the identified likely positive instances from the unlabeled set are indeed positive. However, directly applying GAN is problematic because GAN focuses on only the positive data. The resulting PU learning method will have high precision but low recall. We propose a new objective function based on KL-divergence. Evaluation using both image and text data shows that PAN outperforms state-of-the-art PU learning methods and also a direct adaptation of GAN for PU learning.
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Villalonga, Gabriel, Joost Van de Weijer, and Antonio M. López. "Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition." Sensors 20, no. 3 (January 21, 2020): 583. http://dx.doi.org/10.3390/s20030583.

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On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼ 1 / 4 for new/known classes; even for more challenging ratios such as ∼ 4 / 1 , the results are also very positive.
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Dissertations / Theses on the topic "GAN adaptation"

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Stein, Dylan. "An Arctic Adaptation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1554120044753592.

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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.

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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.
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Lindkvist, Oskar. "Model Adaptation of a Mixed Flow Turbofan Engine." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80667.

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Gas turbine performance models are usually created in an object oriented manner, where different standard components are connected to form the complete model. The characteristics of these components are often represented by component maps and empirical correlations. However, engine specific component characteristics are seldom available to anyone outside of the manufacturers. It is therefore very common for researchers to use publicly accessible or generic component maps instead. But in order to reduce prediction errors the maps have to be modified to fit any specific engine. This thesis work investigates the process of adapting a parametric turbofan engine model to a limited amount of test-data using the propulsion program EVA. Steady state test-data was generated using an initial reference model with SLS operating conditions. Another engine model with different fan, compressor and turbine maps was then used in the adaptation. An initial on-design model was adapted to the highest power test-data point. This model is based on aerothermodynamic equations and is used as a reference to scale the generic component maps to. A sensitivity analysis was done at this point in order to find dependencies between unknown component parameters and test data. These were then included in the cycle solver which employs a version of the Newton-Raphson method. After the fan and compressor maps had been scaled to the design point they were adapted to test-data by adjusting the mass flow parameters in a direct search optimizer. Finally, speed lines in the fan and compressor maps were relabeled to reduce rotor speed errors. The adapted performance model was then validated against the reference model at a few flying conditions. The performance model results demonstrate that it is possible to greatly reduce prediction errors by only adjusting the corrected mass flow in fan and compressor maps. Additionally, rotor speed errors could successfully be corrected as a final step in the adaptation by relabeling speed lines in the component maps. When validated, the adapted model had a maximum parameter error of 1.5%.
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Svanberg, Kerstin. "Bringing the history of fashion up-to-date; towards a model for temporal adatation in translation." Thesis, Linnéuniversitetet, Institutionen för språk och litteratur, SOL, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-22629.

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In cultural adaptation, the translator has a solid theoretical ground to stand upon; scholars have elaborated strategies that are helpful to this effect. However, there is little research, if any, to rely upon in the matter of temporal adaptation. The aim of this paper is to fill this gap. The primary data used in this translational study consists of an English source text that was published in 2008 and the resulting target text, translated to Swedish in 2012. Hence, in order for the target text to function in its time, there was a four-year long time gap to fill with accurate and relevant data and in a style that would not deviate from the author’s original intentions; the target text needed to be temporally adapted. In what follows, I will suggest a set of strategies for temporal adaptation. The model is elaborated with strategies for cultural adaptation as a starting point and based upon measures taken to relocate the target text to 2012. The suggested strategies are time bridging, updating, adjustment and omission. These four strategies make up the model that I put forward to bridge the theoretical gap that seems to prevail in the matter of temporal adaptation. However, considering that the data used in this study was relatively limited, the applicability of the strategies may be the scope of future studies.
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Zhang, Kewen. "Attitude Synchronization of Spacecraft Formation with Optimization and Adaptation of Consensus Penalty Terms." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/246.

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The contribution of this thesis is on the temporal adjustment of the consensus weights, as applied to spacecraft formation control. Such an objective is attained by dynamically enforcing attitude synchronization via coupling terms included in each spacecraft controller. It is assumed that each spacecraft has identical dynamics but with unknown inertia parameters and external disturbances. By augmenting a standard adaptive controller that accounts for the unknown parameters, made feasible via an assumption on parameterization, with adaptation of the consensus weights, one opts to improve spacecraft synchronization. The coupling terms, responsible for enforcing synchronization amongst spacecraft, are weighted dynamically in proportion to the disagreement between the states of the spacecraft. The time adjustment of edge-dependent gains as well as the special cases of node-dependent and agent-independent constant gains are derived using Lyapunov redesign methods. The proposed adaptive control architectures which allow for adaptation of both parameter uncertainties and consensus penalty terms are demonstrated via extensive numerical studies of spacecraft networks with limited connectivity. By considering the sum of deviation-from-the-mean and rotational kinetic energy as appropriate metrics for synchronization and controller performance, the numerical studies also provide insights on the choice of optimal consensus gains.
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Lagarde, Cyril. "Modélisation de transistor de puissance en technologie GaN : conception d’un amplificateur de type Doherty pour les émetteurs à puissance adaptative." Limoges, 2006. https://aurore.unilim.fr/theses/nxfile/default/36416d31-0431-481e-84ec-ad7070a42012/blobholder:0/2006LIMO0038.pdf.

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L’avènement des technologies de transistors de puissance sur matériaux "grand gap" tels que le Nitrure de Gallium (GaN) permet d’envisager un saut technologique majeur pour la génération de puissance à l’état solide. Cette nouvelle technologie présente des possibilités intéressantes pour des amplificateurs de puissance micro-ondes, en termes de température de fonctionnement élevée, de densités de puissance élevées et de tensions de claquage élevées. Dans une première partie, ce travail concerne le développement d'un nouveau modèle non linéaire électrothermique tabulaire comprenant les effets de pièges sur un transistor HEMT AlGaN/GaN. Ce modèle a été alors utilisé, dans la deuxième partie de cette thèse, pour concevoir un amplificateur de puissance basé sur le principe Doherty. Cependant les contraintes de linéarité et de rendement imposées dans les communications spatiales constituent, encore à l’heure actuelle, un obstacle à l’utilisation de ces technologies. Afin de traiter ces contraintes, nous avons proposé et conçu un nouvel amplificateur Doherty ayant une architecture symétrique basée sur trois transistors HEMTs GaN. Les résultats expérimentaux ont montré des possibilités intéressantes de cette nouvelle structure Doherty en termes de rendement et de linéarité
New power transistors technologies based on “wide bandgap” materials such as Gallium Nitride (GaN) were developed these last years. This new technology presents interesting capabilities for high power microwave amplifiers in terms of high working temperature, high power densities and high breakdown voltages. This work concerns first the development of a new tabular electrothermal non linear model including trapping effects on an AlGaN/GaN power HEMT. This model has then been used, in the second part of this thesis, to design a power amplifier based on the Doherty principle. In satellite communication systems, a good linearity and a high efficiency are drastic constraints on the power amplifier. In order to deal with these constraints, we have proposed and designed a new Doherty amplifier with a symmetrical architecture based on three GaN HEMT devices. Experimental results have shown the interesting capabilities of this new Doherty structure in terms of efficiency and linearity under output power back-off operation
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Dunn, Felice Audris. "Gain control of rod and cone vision in the mammalian retina /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10642.

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Karanikas, Konstantinos [Verfasser]. "Adaptation der muskulären Kraftfähigkeiten sowie der Gang- und Laufkinematik nach einer vorderen Kreuzbandrekonstruktion bzw. Kreuzbandverletzung / Konstantinos Karanikas." Köln : Zentralbibliothek der Deutschen Sporthochschule, 2006. http://d-nb.info/1071854135/34.

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Ingallina, Alessandro. "Adaptive High-Gain Observers via Discete time Identifier." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The system's state observation is one of the most important problem in control theory, and it becomes extremely challenging when the system model is not entirely known. For linear systems the problem is solved by using Luenberger observer in a deterministic framework and by Kalman filter in a stochastic framework, while for nonlinear systems, the observation problem is still a research topic. The aim of this thesis is to give a framework, in which the adaptation problem, relative to the model unknowns, can be performed by system identification techniques. In particular, in this thesis we develop and implement adaptive observers design, that uses "universal approximator" to perform the adaptation problem. Moreover, we present simulations on the performance of the proposed observer.
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Rocha, Chrystianne. "Gap filler adaptativo para sistema ISDB-Tb." Universidade Presbiteriana Mackenzie, 2014. http://tede.mackenzie.br/jspui/handle/tede/1440.

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Made available in DSpace on 2016-03-15T19:37:50Z (GMT). No. of bitstreams: 1 Chrystianne Rocha.pdf: 2924776 bytes, checksum: a1d74db138a8b87f0518f3a6ef67c404 (MD5) Previous issue date: 2014-08-07
This master thesis presents the proposal of assigning the cognitive ability to a gap filler. Differently from the usual repeaters found on the market, the adaptive gap filler is able to automatically monitor tuned channels with the intention of verifying if the protection ratio described in Resolution nº398 from Anatel is being respected. Spectrum sensing techniques and the concepts related to coverage area will be addressed. Tests were carried out on the Matlab and GNU Radio Software with the purpose of analyzing the adaptive gap filler performance on real channels.
Este trabalho apresenta a proposta de atribuir ao gap filler a capacidade cognitiva . Diferentemente dos repetidores encontrados no mercado, o gap filler adaptativo tem como função monitorar, de forma autônoma, os canais sintonizados para verificar se a relação de proteção descrita na Resolução nº 398 da Anatel está sendo respeitada. Como fundamentos dessa proposta são abordadas as técnicas de sensoriamento do espectro e os conceitos relacionados às áreas de cobertura. Em uma abordagem prática, os testes foram desenvolvidos no Matlab e no GNU Radio, em que se analisa a atuação do gap filler adaptativo em canais reais.
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Books on the topic "GAN adaptation"

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Découvrons l'énergie: Le charbon / James Strachan; Adaptation française de François Carlier. Saint-Lambert: Les Éditions Héritage Inc., 1985.

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Découvrons l'énergie: Le gaz naturel/ Guy Arnold; Adaptation française de François Carlier. Saint-Lambert: Les Éditions Héritage Inc., 1985.

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Sutton, John R. Hypoxia: The adaptations. Toronto: B.C. Decker, 1990.

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Environmental and Water Resources Institute (U.S.), ed. Climate change modeling, mitigation, and adaptation. Reston, Virginia: American Society of Civil Engineers, 2013.

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Gai bian yu Zhongguo dian ying: Adaptation and Chinese film. Beijing: Zhongguo dian ying chu ban she, 2020.

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Zi yu guang: Wen xue gai bian dian ying tan. Xianggang: San lian shu dian (Xianggang) you xian gong si, 2016.

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Wen xue gai bian dian ying. Xianggang: San lian shu dian (Xianggang) you xian gong si, 2010.

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Ren lei gan zhi he shi ying qi hou bian hua de xing wei xue yan jiu: Yi Jilin Sheng Dunhua Shi xiang cun wei li = Behavioral science based research on human cognition and adaptation to climate change : a case study on rural area in Dunhua City, Jilin Province. Beijing: Zhongguo huan jing ke xue chu ban she, 2011.

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Bert, Metz, Hulme M, Grubb Michael, Netherlands Environmental Assessment Agency, Tyndall Centre for Climate Change Research., and European Forum on Integrated Environmental Assessment., eds. Climate policy options post-2012: European strategy, technology and adaptation after Kyoto. London: Earthscan, 2005.

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Canada Oil and Gas Lands Administration. Local business adaptation to east coast offshore energy development. Ottawa: Environmental Research Funds, 1987.

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Book chapters on the topic "GAN adaptation"

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Huang, Sheng-Wei, Che-Tsung Lin, Shu-Ping Chen, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. "AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation." In Computer Vision – ECCV 2018, 731–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_44.

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Huang, Sheng-Wei, Che-Tsung Lin, Shu-Ping Chen, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. "Correction to: AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation." In Computer Vision – ECCV 2018, E1. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_50.

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Yan, Wenjun, Yuanyuan Wang, Shengjia Gu, Lu Huang, Fuhua Yan, Liming Xia, and Qian Tao. "The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN." In Lecture Notes in Computer Science, 623–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32245-8_69.

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Xie, Xinpeng, Jiawei Chen, Yuexiang Li, Linlin Shen, Kai Ma, and Yefeng Zheng. "MI$$^2$$GAN: Generative Adversarial Network for Medical Image Domain Adaptation Using Mutual Information Constraint." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 516–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_50.

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Kumar, Shushant, and K. Chandrasekaran. "LRSS-GAN: Long Residual Paths and Short Skip Connections Generative Adversarial Networks for Domain Adaptation and Image Inpainting." In Advances in Intelligent Systems and Computing, 293–303. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6984-9_24.

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Richter, Hanz. "Gain Scheduling and Adaptation." In Advanced Control of Turbofan Engines, 91–110. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1171-0_5.

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Davies-Vollum, K. Sian, Debadayita Raha, and Daniel Koomson. "Climate Change Impact and Adaptation: Lagoonal Fishing Communities in West Africa." In African Handbook of Climate Change Adaptation, 2221–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_221.

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AbstractLagoons are a common feature of the low-lying West African coastline. These lagoons are resource-rich and biodiverse. The small-scale fishing communities, which border them, are dependent on the resources and ecosystem services for their livelihoods and well-being. Climate change has had significant and diverse effects on both the lagoons and their surrounding communities. Sea level rise has caused erosion of the coast and increased the risk of floods. Changes to rainfall patterns have caused shifts in lagoon ecosystems and physical cycles. Of particular relevance to lagoon fishing communities is the fluctuation in quantity and distribution of fish catch that they rely upon for economic livelihood. Understanding the vulnerability of these communities to the effects of climate change is critical to supporting and developing successful adaptations. Using a case study from Ghana, sustainable livelihoods approach (SLA) and vulnerability framework are used to characterize the community vulnerability, giving insight into the temporal and spatial dynamics of vulnerability and how subsections of the community may be identified and prioritized for adaptation interventions. A scalar analysis of the relevant coastal and environmental frameworks and policy to support climate change adaptation in coastal communities reveals the common challenges in implementing adaptation interventions and strategies in the region. A policy gap exists between high level, institutional coastal, and climate directives and implementation of climate adaptations at the local level. That gap might be bridged by a participatory approach that places coastal communities at the center of creating and enacting climate change adaptations.
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Davies-Vollum, K. Sian, Debadayita Raha, and Daniel Koomson. "Climate Change Impact and Adaptation: Lagoonal Fishing Communities in West Africa." In African Handbook of Climate Change Adaptation, 1–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_221-1.

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AbstractLagoons are a common feature of the low-lying West African coastline. These lagoons are resource-rich and biodiverse. The small-scale fishing communities, which border them, are dependent on the resources and ecosystem services for their livelihoods and well-being. Climate change has had significant and diverse effects on both the lagoons and their surrounding communities. Sea level rise has caused erosion of the coast and increased the risk of floods. Changes to rainfall patterns have caused shifts in lagoon ecosystems and physical cycles. Of particular relevance to lagoon fishing communities is the fluctuation in quantity and distribution of fish catch that they rely upon for economic livelihood. Understanding the vulnerability of these communities to the effects of climate change is critical to supporting and developing successful adaptations. Using a case study from Ghana, sustainable livelihoods approach (SLA) and vulnerability framework are used to characterize the community vulnerability, giving insight into the temporal and spatial dynamics of vulnerability and how subsections of the community may be identified and prioritized for adaptation interventions. A scalar analysis of the relevant coastal and environmental frameworks and policy to support climate change adaptation in coastal communities reveals the common challenges in implementing adaptation interventions and strategies in the region. A policy gap exists between high level, institutional coastal, and climate directives and implementation of climate adaptations at the local level. That gap might be bridged by a participatory approach that places coastal communities at the center of creating and enacting climate change adaptations.
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Verdon-Kidd, Danielle C., Anthony S. Kiem, and Emma K. Austin. "Bridging the gap between researchers and decision-makers." In Applied Studies in Climate Adaptation, 51–59. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118845028.ch7.

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Akachukwu, Doris, Michael Adedapo Gbadegesin, Philippa Chinyere Ojimelukwe, and Christopher John Atkinson. "Biochar for Climate Change Adaptation: Effect on Heavy Metal Composition of Telfairia occidentalis Leaves." In African Handbook of Climate Change Adaptation, 1401–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_202.

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AbstractGas flaring is a key contributor of greenhouse gases that causes global warming and climate change. Adaptation measures for tackling impacts of climate change have gained much research interest. This chapter assessed vegetable farmers’ perception of gas flaring and the effect of biochar remediation on the heavy metal composition of cultivated Telfairia occidentalis. A gas-flared area, Ohaji/Egbema L.G.A of Imo State, and a non-gas-flared area, Umudike, Ikwuano L.G.A, were selected for this research. Structured questionnaire was used to elicit information from 120 respondents. Soils were collected from the study sites and transported to the greenhouse. Five different rates, 0 t ha−1, 7.1 t ha−1, 13.9 t ha−1, 20.9 t ha−1, and 28.0 t ha−1, of palm bunch biochar were applied to the soils in plastic buckets. After 2 weeks of mineralization, two viable seeds of Telfairia occidentalis were planted in each bucket and watered every other day for 8 weeks. The result revealed that 63% of vegetable farmers where female, while 37% were male in the gas-flared area. A total of 97% of the farmers had knowledge of gas flaring. A total decrease of 55% percent income, 90% yield, and 67% market quality of vegetable farmers was attributed to gas-flared activities. The plant height of cultivated vegetables increased every 2 weeks with greater increase in the test plant. Heavy metal concentration (Pb, and Cr) decreased with increasing biochar rate and was significantly lower for 28.0 t ha−1. Biochar can enhance soil fertility and help immobilize heavy metals. The effect of biochar application on the heavy metal composition is dependent on the rate of application. Biochar use could be a cheap adaptation measure in the face of a changing climate.
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Conference papers on the topic "GAN adaptation"

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Leno da Silva, Felipe, Ruben Glatt, Raphael Cobe, and Renato Vicente. "GAN-based Data Mapping for Model Adaptation." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/2021072415.

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Bejiga, Mesay Belete, and Farid Melgani. "Gan-Based Domain Adaptation for Object Classification." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518649.

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Menke, Maximilian, Thomas Wenzel, and Andreas Schwung. "Improving GAN-based Domain Adaptation for Object Detection." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922138.

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Sun, Yunzhe, Gang Yang, Dayong Ding, Gangwei Cheng, Jieping Xu, and Xirong Li. "A GAN-based Domain Adaptation Method for Glaucoma Diagnosis." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207358.

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Ma, Di, Fan Zhang, and David R. Bull. "Gan-Based Effective Bit Depth Adaptation for Perceptual Video Compression." In 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020. http://dx.doi.org/10.1109/icme46284.2020.9102865.

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Zhang, Mingxu, Hongxia Wang, Peisong He, Asad Malik, and Hanqing Liu. "Improving GAN-Generated Image Detection Generalization Using Unsupervised Domain Adaptation." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859763.

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Voreiter, Claire, Jean-Christophe Burnel, Pierre Lassalle, Marc Spigai, Romain Hugues, and Nicolas Courty. "A Cycle Gan Approach for Heterogeneous Domain Adaptation in Land Use Classification." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9324264.

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Liu, Yujie, Xing Wei, Yang Lu, Chong Zhao, and Xuanyuan Qiao. "Source Free Domain Adaptation via Combined Discriminative GAN Model for Image Classification." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9891979.

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Hoang, Hien Do, Do Thi Thu Hien, Thai Bui Xuan, Tri Nguyen Ngoc Minh, Phan The Duy, and Van-Hau Pham. "DA-GAN: Domain Adaptation for Generative Adversarial Networks-assisted Cyber Threat Detection." In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2022. http://dx.doi.org/10.1109/rivf55975.2022.10013804.

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Choi, Jaehoon, Taekyung Kim, and Changick Kim. "Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00693.

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Reports on the topic "GAN adaptation"

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Macura, Biljana, Nella Canales, Inès Bakhtaoui, Richard Taylor, Elvine Kwamboka, Rocio Diaz-Chavez, Fedra Vanhuyse, et al. Effectiveness of climate change adaptation interventions in sub-Saharan Africa and the impact of funding modalities: a mixed methods systematic review protocol. Stockholm Environment Institute, October 2021. http://dx.doi.org/10.51414/sei2021.021.

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International climate finance plays a key role in enabling the implementation of adaptation measures. However, while there is a common metric for gauging the effectiveness of finance for mitigation – greenhouse gas emission reduction per unit of funding – no corresponding metric exists for adaptation. Instead, assessments of what works best in adaptation finance focus either on procedural aspects of funding modalities, such as equity in the allocation of funding, or on the extent to which specific adaptation activities produce the desired results. This mixed methods systematic review aims to assess the effectiveness of adaptation finance and bridge the gap between those two approaches. It involves a transparent and comprehensive synthesis of the academic and grey literature on how different characteristics of adaptation projects in sub-Saharan Africa – and finance for those projects – affect adaptation outcomes, particularly in terms of risk and vulnerability to climate change impacts. Finalised adaptation projects funded by a set of the multilateral climate funds and two bilateral donors (United Kingdom and Sweden) are the focus of this review. The findings can help inform the future design and implementation of adaptation activities as well as funding decisions.
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Melloni, Gian. Report on Webinar Series: Sanitation Programmes Throughout and Beyond the Covid-19 Pandemic. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/slh.2021.026.

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In discussions on sanitation and Covid-19, the continuity of long-term sanitation goals throughout and beyond the pandemic has been rarely considered. To respond to this gap, the Sanitation Learning Hub (SLH) and UNICEF hosted a webinar series to share knowledge and experience on ways programmes have changed and adapted. The two webinars presented examples of initiatives which have continued to pursue long-term sanitation objectives during the pandemic, with successes, setbacks and adaptations, and space was provided to reflect on possible future impacts of Covid-19 on sanitation planning, implementation and monitoring. This report was written by Gian Melloni (Research Officer, Sanitation Learning Hub).
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Moore, Winston, J. Enrique Chueca, Veronica R. Prado, Michelle Carvalho Metanias Hallack, and Laura Giles Álvarez. Energy Transition in Barbados: Opportunities for Adaptation of Energy Taxes to Mitigate Loss of Government Revenue. Inter-American Development Bank, November 2022. http://dx.doi.org/10.18235/0004534.

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Barbados, through its Barbados National Energy Policy (BNEP) 2019-2030, announced its commitment to achieving 100 percent renewable energy and carbon neutrality by 2030. This commitment creates an opportunity for the GoB to manage the impact of the transition toward renewable clean energy by introducing measures to transform the way revenue from energy is collected thereby avoiding unnecessary fiscal costs. The purpose of this study is to calculate the revenue gap derived from Barbados 2030 energy transition goal of having a revenue-neutral transition and propose and evaluate various policy measures that could help seize opportunities to close that gap. The simulation model suggests that the energy transition would result in an estimated BBD$105 million in revenue losses a year by following the BNEP. Such a reduction would create a significant fiscal gap that would need to be addressed through the introduction of new forms of taxes or changes to current taxes in order to adapt tax collection to revenue creation from the new clean energy economy. A wide range of tax policy options and issues surrounding their effective implementation were discussed such as: increased taxes on fossil fuels, a change in the VAT rate, mileage taxes on electric and hybrid vehicles, and taxes on renewable energy production. Each of these new tax approaches can help address the fiscal gap estimated above.
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Lambert, J. D. B., K. C. Gross, E. V. Depiante, E. L. Callis, and P. M. Egebrecht. Adaptation of gas tagging for failed fuel identification in light water reactors. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/224305.

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Anderson, Iver, and John Siemon. Specific Adaptation of Gas Atomization Processing for Al-Based Alloy Powder for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1373366.

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Anderson, Iver, and John Siemon. Specific Adaptation of Gas Atomization Processing for Al-Based Alloy Powder for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1415042.

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Maston, V. A. Adaptation of a commercially available 200 kW natural gas fuel cell power plant for operation on a hydrogen rich gas stream. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/670180.

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Babiker, Mustafa, Amir Bazaz, Paolo Bertoldi, Felix Creutzig, Heleen De Coninck, Kiane De Kleijne, Shobhakar Dhakal, et al. What the Latest Science on Climate Change Mitigation means for Cities and Urban Areas. Indian Institute for Human Settlements, October 2022. http://dx.doi.org/10.24943/supsv310.2022.

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The Summary for Urban Policymakers (SUP) initiative provides a distillation of the IPCC reports into accessible and targeted summaries that can help inform action at city and regional scales. Volume I in the series, What the Latest Physical Science of Climate Change Means for Cities, identified the ways in which human-induced climate change is affecting every region of the world, and the cities and urban areas therein. Volume II, Climate Change in Cities and Urban Areas: Impacts, Adaptation and Vulnerability, assessed the feasibility and effectiveness of different adaptation options. To achieve climate resilient development, synergies between policies and actions for climate change adaptation, mitigation and other development goals are needed. This third volume in the series, What the Latest Science on Climate Change Mitigation Means For Cities and Urban Areas offers a concise and accessible distillation of the IPCC Working Group III Report for urban policymakers. The 21st century is characterized by a rapidly growing urban population, urban land expansion and associated rise in demand for resources, infrastructure and services. These trends are expected to drive the growth in emissions from urban consumption and production through 2100, although the rate of urban emissions growth will depend on the type of urbanisation and the speed and scale of mitigation action implemented. Aggressive and ambitious policies for transition towards net zero greenhouse gas emissions can be implemented in cities and urban areas, while contributing to sustainable development. Ultimately, mitigation action and adaptation are interdependent processes, and pursuing these actions together can promote sustainable development.
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Fitzpatrick, Rachael, and Helen West. Improving Resilience, Adaptation and Mitigation to Cimate Change Through Education in Low- and Lower-middle Income Countries. Institute of Development Studies, March 2022. http://dx.doi.org/10.19088/k4d.2022.083.

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Climate resilience is the ability to anticipate, prepare for, and respond to hazardous events, trends, or disturbances related to climate (C2ES, 2022). Mitigation focuses on reducing the human impacts contributing to climate change (Burton, 2007, cited in Rousell & Cutter-Mackenzie-Knowles, 2020). Adaptation is about increasing people’s adaptive capacity, reducing the vulnerability of communities and managing risks (Anderson, 2012). Anderson further defines adaptation as not just being able to adapt from one stable climate to another but having the skills to adapt to uncertainty and make informed decisions in a changing environment. While ‘climate change’ is the term used throughout these briefs, it should be read as a shorthand for a more inclusive approach, which also captures associated environmental degradation. The Intergovernmental Panel on Climate Change (IPCC) warned, in their latest report, that global surface temperatures will continue to increase until 2050 (IPCC, 2021, p. 17). This will take place regardless of human intervention to reduce greenhouse gas emissions. The report also warns that the traditional technocratic approaches are insufficient to tackle the challenge of climate change, and that greater focus on the structural causes is needed. High- and upper-middle-income countries have been persistently shown to be the biggest contributors to the global carbon dioxide emissions, with lower income countries facing the most disruptive climate hazards, with Africa countries particularly vulnerable (CDP, 2020; IPCC, 2021). The vulnerability of low-income contexts exacerbates this risk, as there is often insufficient infrastructure and resources to ensure resilience to climate hazards (IPCC, 2021). For decades, advocates of climate change education have been highlighting the potential of education to help mitigate against climate change, and support adaptation efforts. However, implementation has been patchy, with inconsistent approaches and a lack of evidence to help determine the most effective way forward.This paper is divided into three sections, drawing together evidence on the key aspects of system reform,green and resilient infrastructure and Curriculum, pedagogy, assessment and teacher development.
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Morandi, Paula, and Amy Lewis. 2021 IDB Climate Finance Database. Inter-American Development Bank, December 2022. http://dx.doi.org/10.18235/0004645.

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Under the current IDBG Corporate Results Framework (CRF) 2020-2023 (https://crf.iadb.org/en), the IDB committed to reach 30% of the total amount approved (including all lending operations) of climate finance during this period. In 2021, the IDB Group - composed of the IDB, IDB Lab (formerly the Multilateral Investment Fund) and IDB Invest - approved US$6 billion in climate finance as per the MDB climate finance tracking methodology. This resource is aimed at development activities carried out by the public and private sectors that reduce greenhouse gas (GHG) emissions and thus mitigate climate change, and/or that reduce vulnerability to climate change and contribute to an adaptation process. The IDB only climate finance in 2021 was equivalent to US$ 4.5 billion.
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