Relevant bibliographies by topics / Domain adaption

Contents

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

Academic literature on the topic 'Domain adaption'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Domain adaption"

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Li, Linhao, Zhiqiang Zhou, Bo Wang, Lingjuan Miao, Zhe An, and Xiaowu Xiao. "Domain Adaptive Ship Detection in Optical Remote Sensing Images." Remote Sensing 13, no. 16 (2021): 3168. http://dx.doi.org/10.3390/rs13163168.

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With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between diff
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Guo, Rui, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu, and Xunhui Zhang. "Unsupervised spatial-awareness attention-based and multi-scale domain adaption network for point cloud classification." International Journal of Wavelets, Multiresolution and Information Processing 19, no. 04 (2021): 2150007. http://dx.doi.org/10.1142/s0219691321500077.

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Domain adaption is a special transfer learning method, whose source domain and target domain generally have different data distribution, but need to complete the same task. There have been many significant types of research on domain adaptation in 2D images, but in 3D data processing, domain adaptation is still in its infancy. Therefore, we design a novel domain adaptive network to complete the unsupervised point cloud classification task. Specifically, we propose a multi-scale transform module to improve the feature extractor. Besides, a spatial-awareness attention module combined with channe
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Deng, Wan-Yu, Yu-Tao Qu, and Qian Zhang. "Domain Adaption Based on ELM Autoencoder." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1239164.

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We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance. The widely experimental results on Office/Caltech-256 datasets s
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Lu, Nannan, Fei Chu, Haoran Qi, and Shuang Xia. "A new domain adaption algorithm based on weights adaption from the source domain." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 12 (2018): 1769–76. http://dx.doi.org/10.1002/tee.22739.

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M., Dhanashree, and R. N. Phursule. "Online Cost Sensitive Domain Knowledge Adaption." International Journal of Computer Applications 123, no. 18 (2015): 16–18. http://dx.doi.org/10.5120/ijca2015905639.

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Foxall, Gordon R., and Isabelle Szmigin. "Adaption-Innovation and Domain-Specific Innovativeness." Psychological Reports 84, no. 3 (1999): 1029–30. http://dx.doi.org/10.2466/pr0.1999.84.3.1029.

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Reported is a study of the relationship between a global measure of adaption-innovation (Kirton's Adaption-Innovation Inventory) and a domain-specific measure of consumers' innovativeness (Goldsmith and Hofacker's Domain Specific Innovativeness Scale). For a convenience sample of 26 adult consumers there was, as expected, no significant correlation between scores on the two scales.
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Ma, Chenhui, Dexuan Sha, and Xiaodong Mu. "Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification." Remote Sensing 13, no. 7 (2021): 1270. http://dx.doi.org/10.3390/rs13071270.

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Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source-domain-specific boundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary o
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Dong, Le, Ning Feng, Pinjie Quan, Gaipeng Kong, Xiuyuan Chen, and Qianni Zhang. "Optimal kernel choice for domain adaption learning." Engineering Applications of Artificial Intelligence 51 (May 2016): 163–70. http://dx.doi.org/10.1016/j.engappai.2016.01.022.

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Wang, Yan, Serguei Pakhomov, James O. Ryan, and Genevieve B. Melton. "Domain adaption of parsing for operative notes." Journal of Biomedical Informatics 54 (April 2015): 1–9. http://dx.doi.org/10.1016/j.jbi.2015.01.016.

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Li, Rumeng, Xun Wang, and Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8245–52. http://dx.doi.org/10.1609/aaai.v34i05.6339.

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Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational
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Dissertations / Theses on the topic "Domain adaption"

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Pettersson, Harald. "Sentiment analysis and transfer learning using recurrent neural networks : an investigation of the power of transfer learning." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161348.

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In the field of data mining, transfer learning is the method of transferring knowledge from one domain into another. Using reviews from prisjakt.se, a Swedish price comparison site, and hotels.com this work investigate how the similarities between domains affect the results of transfer learning when using recurrent neural networks. We test several different domains with different characteristics, e.g. size and lexical similarity. In this work only relatively similar domains were used, the same target function was sought and all reviews were in Swedish. Regardless, the results are conclusive; t
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Holm, Henrik. "Bidirectional Encoder Representations from Transformers (BERT) for Question Answering in the Telecom Domain. : Adapting a BERT-like language model to the telecom domain using the ELECTRA pre-training approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301313.

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The Natural Language Processing (NLP) research area has seen notable advancements in recent years, one being the ELECTRA model which improves the sample efficiency of BERT pre-training by introducing a discriminative pre-training approach. Most publicly available language models are trained on general-domain datasets. Thus, research is lacking for niche domains with domain-specific vocabulary. In this paper, the process of adapting a BERT-like model to the telecom domain is investigated. For efficiency in training the model, the ELECTRA approach is selected. For measuring target- domain perfor
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Feyrer, Hubert. "System administration training in the virtual unix lab an e-learning system with diagnosis via a domain specific language as base for an architecture for tutorial assistance and user adaption." Aachen Shaker, 2008. http://d-nb.info/992564581/04.

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Sundström, Johan. "Sentiment analysis of Swedish reviews and transfer learning using Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-339066.

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Sentiment analysis is a field within machine learning that focus on determine the contextual polarity of subjective information. It is a technique that can be used to analyze the "voice of the customer" and has been applied with success for the English language for opinionated information such as customer reviews, political opinions and social media data. A major problem regarding machine learning models is that they are domain dependent and will therefore not perform well for other domains. Transfer learning or domain adaption is a research field that study a model's ability of transferring k
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Feyrer, Hubert [Verfasser]. "System Administration Training in the Virtual Unix Lab : An e-learning system with diagnosis via a domain specific language as base for an architecture for tutorial assistance and user adaption / Hubert Feyrer." Aachen : Shaker, 2009. http://d-nb.info/1161309985/34.

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Collins, Gordon. "Invariant adaptive domain methods." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245511.

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Hake, Michael James. "Spectroscopic Characterization of the Interaction of Nck Domains with the Epidermal Growth Factor Receptor Juxtamembrane Domain." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1207340174.

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Sandu, Oana. "Domain adaptation for summarizing conversations." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33932.

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The goal of summarization in natural language processing is to create abridged and informative versions of documents. A popular approach is supervised extractive summarization: given a training source corpus of documents with sentences labeled with their informativeness, train a model to select sentences from a target document and produce an extract. Conversational text is challenging to summarize because it is less formal, its structure depends on the modality or domain, and few annotated corpora exist. We use a labeled corpus of meeting transcripts as the source, and attempt to summa
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Ye, Lei. "Adaptive frequency-domain access techniques." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479512.

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Htike, Kyaw Kyaw. "Domain adaptation for pedestrian detection." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/7290/.

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Object detection is an essential component of many computer vision systems. The increase in the amount of collected digital data and new applications of computer vision have generated a demand for object detectors for many different types of scenes digitally captured in diverse settings. The appearance of objects captured across these different scenarios can vary significantly, causing readily available state-of-the-art object detectors to perform poorly in many of the scenes. One solution is to annotate and collect labelled data for each new scene and train a scene-specific object detector th
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Books on the topic "Domain adaption"

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Singh, Richa, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, eds. Domain Adaptation for Visual Understanding. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30671-7.

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Csurka, Gabriela, ed. Domain Adaptation in Computer Vision Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58347-1.

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Hesthaven, Jan S. A waverlet optimized adaptive multi-domain method. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Hesthaven, J. S. A wavelet optimized adaptive multi-domain method. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Venkateswara, Hemanth, and Sethuraman Panchanathan, eds. Domain Adaptation in Computer Vision with Deep Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3.

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Adamo, Ronald C. Adaptive windows via Kalman filtering in the spectral domain. Naval Postgraduate School, 1991.

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Albarqouni, Shadi, Spyridon Bakas, Konstantinos Kamnitsas, et al., eds. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60548-3.

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Bunt, Harry C. Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing. Springer Science+Business Media B.V., 2011.

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Chen, Lin. A phase domain adaptive tracking bandpass filter for power engineering applications. National Library of Canada, 1993.

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Nikitakos, Nikitas V. A comparison of two frequency domain adaptive beamforming algorithms for sonar signal processing. Naval Postgraduate School, 1988.

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Book chapters on the topic "Domain adaption"

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Dong, Nanqing, and Eric P. Xing. "Domain Adaption in One-Shot Learning." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10925-7_35.

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Shi, Yue, Martha Larson, and Alan Hanjalic. "Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering." In User Modeling, Adaption and Personalization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4_26.

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Wei, Yingcan. "Transferable Adversarial Cycle Alignment for Domain Adaption." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_52.

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Ye, Ziyu, Chen Ju, Chaofan Ma, and Xiaoyun Zhang. "Unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentation." In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4_13.

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Wang, Haijian, Meng Yang, Hui Li, and Linbin Ye. "Person ReID: Optimization of Domain Adaption Though Clothing Style Transfer Between Datasets." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_43.

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Kang, Peiran, Xiaorui Ma, and Hongyu Wang. "Object Detection of Remote Sensing Image Based on Multi-level Domain Adaption." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_116.

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Fu, Biying, Naser Damer, Florian Kirchbuchner, and Arjan Kuijper. "Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-Adaption and Few-Shot Learning." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68799-1_15.

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Xiang, Yaoci, Chong Zhao, Xing Wei, Yang Lu, and Shaofan Liu. "Multi-step Domain Adaption Image Classification Network via Attention Mechanism and Multi-level Feature Alignment." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86137-7_2.

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Hoffman, Judy, Brian Kulis, Trevor Darrell, and Kate Saenko. "Discovering Latent Domains for Multisource Domain Adaptation." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33709-3_50.

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Zheng, Jiannan, Shun Miao, and Rui Liao. "Learning CNNs with Pairwise Domain Adaption for Real-Time 6DoF Ultrasound Transducer Detection and Tracking from X-Ray Images." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_73.

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Conference papers on the topic "Domain adaption"

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Agirre, Eneko, and Oier Lopez de Lacalle. "Supervised domain adaption for WSD." In the 12th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609071.

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Liu, Anan, Shu Xiang, Wenhui Li, Weizhi Nie, and Yuting Su. "Cross-Domain 3D Model Retrieval via Visual Domain Adaption." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/115.

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Recent advances in 3D capturing devices and 3D modeling software have led to extensive and diverse 3D datasets, which usually have different distributions. Cross-domain 3D model retrieval is becoming an important but challenging task. However, existing works mainly focus on 3D model retrieval in a closed dataset, which seriously constrain their implementation for real applications. To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation. This method can inherit the advantage of deep learning to learn multi-view visual features in the data-d
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Liu, Jun-Tong, Fang-Yu Wu, Wen-Jin Lu, and Bai-Ling Zhang. "Domain Adaption for Facial Expression Recognition." In 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. http://dx.doi.org/10.1109/icmlc48188.2019.8949178.

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Yang, Yuchen, and Nilanjan Ray. "Foreground-focused domain adaption for object detection." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412906.

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Kovacs, L., and G. Kusper. "Adaption of NNS classification to domain of category values." In 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY 2013). IEEE, 2013. http://dx.doi.org/10.1109/sisy.2013.6662590.

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Zhuo Sun, Cheng Wang, Peng Li, Hanyun Wang, and Jonathan Li. "Hyperspectral image classification with SVM-based domain adaption classifiers." In 2012 International Conference on Computer Vision in Remote Sensing (CVRS). IEEE, 2012. http://dx.doi.org/10.1109/cvrs.2012.6421273.

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Ding, Zhengming, Ming Shao, and Yun Fu. "Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/767.

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Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, i
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Zhu, Shaolin, Yating Yang, Xiao Li, et al. "Domain adaption based on lda and word embedding in SMT." In 2017 International Conference on Asian Language Processing (IALP). IEEE, 2017. http://dx.doi.org/10.1109/ialp.2017.8300561.

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Golda, Thomas, Andreas Blattmann, Jürgen Metzler, and Jürgen Beyerer. "Image domain adaption of simulated data for human pose estimation." In Artificial Intelligence and Machine Learning in Defense Applications II, edited by Judith Dijk. SPIE, 2020. http://dx.doi.org/10.1117/12.2573888.

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Li, Daowei, Kui Fu, Yifan Zhao, Long Xu, and Jia Li. "Cross-Domain Visual Attention Model Adaption with One-Shot GAN." In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2020. http://dx.doi.org/10.1109/mipr49039.2020.00011.

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Reports on the topic "Domain adaption"

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Lei, Xin, Wen Wang, and Andreas Stolcke. Unsupervised Domain Adaptation with Multiple Acoustic Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada630345.

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Moore, Frank, Pat Marshall, and Eric Balster. Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada427113.

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Em Karniadakis, George. An Adaptive Random Domain Decomposition Method for Stochastic CFD and MHD Problems. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada586697.

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Atighetchi, Michael, Jonathan Webb, Partha Pal, Joseph Loyall, Azer Bestavros, and Michael J. Mayhew. Dynamic Cross Domain Information Sharing - A Concept Paper on Flexible Adaptive Policy Management. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada556029.

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Turner, C. David, Joseph Daniel Kotulski, and Michael Francis Pasik. Adaptive mesh refinement for time-domain electromagnetics using vector finite elements :a feasibility study. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/875969.

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Judd, Kenneth, Lilia Maliar, Serguei Maliar, and Rafael Valero. Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19326.

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Sottilare, Robert, Anne Sinatra, Michael Boyce, and Arthur Graesser. Domain Modeling for Adaptive Training and Education in Support of the US Army Learning Model-Research Outline. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada618706.

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McGregor, Mark U., Christian D. Schunn, and Lelyn D. Saner. Expertise as Effective Strategy Use: Testing the Adaptive Strategies Model in the III-Structured Domain of Leadership. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada472099.

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Antonio, John K. Configuring Embeddable Adaptive Computing Systems for Multiple Application Domains with Minimal Size, Weight, and Power. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada418681.

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Clark, Andrew E. Demography and well-being. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/populationyearbook2021.deb02.

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Demography studies the characteristics of populations. One such characteristic is well-being: this was the subject of the 2019 Wittgenstein Conference. Here, I discuss how objective well-being domains can be summarised to produce an overall well-being score, and how taking self-reported (subjective) well-being into account may help in this effort. But given that there is more than one type of subjective well-being score, we would want to know which one is “best”. We would also need to decide whose well-being counts, or counts more than that of others. Finally, I briefly mention the potential r
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