Auswahl der wissenschaftlichen Literatur zum Thema „Domain generalisation“
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Zeitschriftenartikel zum Thema "Domain generalisation"
Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales und Tao Xiang. „Deep Domain-Adversarial Image Generation for Domain Generalisation“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 07 (03.04.2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.
Der volle Inhalt der QuelleSeemakurthy, Karthik, Charles Fox, Erchan Aptoula und Petra Bosilj. „Domain Generalised Faster R-CNN“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 2 (26.06.2023): 2180–90. http://dx.doi.org/10.1609/aaai.v37i2.25312.
Der volle Inhalt der QuelleLe, Hoang Son, Rini Akmeliawati und Gustavo Carneiro. „Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 18 (18.05.2021): 15821–22. http://dx.doi.org/10.1609/aaai.v35i18.17907.
Der volle Inhalt der QuelleRoeper, P. „Generalisation of first-order logic to nonatomic domains“. Journal of Symbolic Logic 50, Nr. 3 (September 1985): 815–38. http://dx.doi.org/10.2307/2274334.
Der volle Inhalt der QuelleGomathi, R., und S. Selvakumaran. „A Novel Medical Image Segmentation Model with Domain Generalization Approach“. International Journal of Electrical and Electronics Research 10, Nr. 2 (30.06.2022): 312–19. http://dx.doi.org/10.37391/ijeer.100242.
Der volle Inhalt der QuelleMyers, Scott, und Jaye Padgett. „Domain generalisation in artificial language learning“. Phonology 31, Nr. 3 (Dezember 2014): 399–433. http://dx.doi.org/10.1017/s0952675714000207.
Der volle Inhalt der QuelleHYLAND, MARTIN. „Some reasons for generalising domain theory“. Mathematical Structures in Computer Science 20, Nr. 2 (25.03.2010): 239–65. http://dx.doi.org/10.1017/s0960129509990375.
Der volle Inhalt der QuelleRashid, Fariza, Ben Doyle, Soyeon Caren Han und Suranga Seneviratne. „Phishing URL detection generalisation using Unsupervised Domain Adaptation“. Computer Networks 245 (Mai 2024): 110398. http://dx.doi.org/10.1016/j.comnet.2024.110398.
Der volle Inhalt der QuelleAnthony, Martin, Peter Bartlett, Yuval Ishai und John Shawe-Taylor. „Valid Generalisation from Approximate Interpolation“. Combinatorics, Probability and Computing 5, Nr. 3 (September 1996): 191–214. http://dx.doi.org/10.1017/s096354830000198x.
Der volle Inhalt der QuelleOrazov, B. B. „On the asymptotic behaviour at infinity of solutions of the traction boundary value problem“. Proceedings of the Royal Society of Edinburgh: Section A Mathematics 111, Nr. 1-2 (1989): 33–52. http://dx.doi.org/10.1017/s0308210500024999.
Der volle Inhalt der QuelleDissertationen zum Thema "Domain generalisation"
Rahman, Mohammad Mahfujur. „Deep domain adaptation and generalisation“. Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205619/1/Mohammad%20Mahfujur_Rahman_Thesis.pdf.
Der volle Inhalt der QuelleMcLean, David. „Improving generalisation in continuous data domains“. Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283816.
Der volle Inhalt der QuelleGuesdon, Romain. „Estimation de poses humaines par apprentissage profond : application aux passagers des véhicules autonomes“. Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20002.
Der volle Inhalt der QuelleResearch into autonomous cars has made great strides in recent decades, focusing particularly on analysis of the external environment and driving-related tasks. This has led to a significant increase in the autonomy of private vehicles. In this new context, it may be relevant to take an interest in the passengers of these autonomous vehicles, to study their behavior in the face of this revolution in the means of transport. The AURA AutoBehave project has been set up to explore these issues in greater depth. This project brings together several laboratories conducting research in different scientific disciplines linked to this theme, such as computer vision, biomechanics, emotions, and transport economics. This thesis carried out at the LIRIS laboratory is part of this project, in which we focus on methods for estimating the human poses of passengers using deep learning. We first looked at state-of-the-art solutions and developed both a dataset and a metric better suited to the constraints of our context. We also studied the visibility of the keypoints to help estimate the pose. We then tackled the problem of domain generalisation for pose estimation to propose an efficient solution under unknown conditions. Thus, we focused on the generation of synthetic passenger data for pose estimation. Among other things, we studied the application of generative networks and 3D modeling methods to our problem. We have used this data to propose different training strategies and two new network architectures. The proposed fusion approach associated with the training strategies makes it possible to take advantage of both generic and specific datasets, to improve the generalisation capabilities of pose estimation methods inside a car, particularly on the lower body
Bücher zum Thema "Domain generalisation"
Aubreville, Marc, David Zimmerer und Mattias Heinrich, Hrsg. Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3.
Der volle Inhalt der QuelleAubreville, Marc, David Zimmerer und Mattias Heinrich. Biomedical Image Registration, Domain Generalisation and Out-Of-Distribution Analysis: MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, Proceedings. Springer International Publishing AG, 2022.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Domain generalisation"
Fick, Rutger H. J., Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit und Saima Ben Hadj. „Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 40–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_5.
Der volle Inhalt der QuelleLong, Xi, Ying Cheng, Xiao Mu, Lian Liu und Jingxin Liu. „Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 73–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_11.
Der volle Inhalt der QuelleAlmahfouz Nasser, Sahar, Nikhil Cherian Kurian und Amit Sethi. „Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 77–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_12.
Der volle Inhalt der QuelleWilm, Frauke, Christian Marzahl, Katharina Breininger und Marc Aubreville. „Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 5–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_1.
Der volle Inhalt der QuelleChung, Youjin, Jihoon Cho und Jinah Park. „Domain-Robust Mitotic Figure Detection with Style Transfer“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 23–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_3.
Der volle Inhalt der QuelleYang, Sen, Feng Luo, Jun Zhang und Xiyue Wang. „Sk-Unet Model with Fourier Domain for Mitosis Detection“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 86–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_14.
Der volle Inhalt der QuelleJahanifar, Mostafa, Adam Shepard, Neda Zamanitajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas und Nasir Rajpoot. „Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 48–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_6.
Der volle Inhalt der QuelleNateghi, Ramin, und Fattaneh Pourakpour. „Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 32–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_4.
Der volle Inhalt der QuelleLafarge, Maxime W., und Viktor H. Koelzer. „Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 62–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_9.
Der volle Inhalt der QuelleTan, Jeremy, Turkay Kart, Benjamin Hou, James Batten und Bernhard Kainz. „MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision“. In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 119–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_18.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Domain generalisation"
Nguyen, Toan, Kien Do, Bao Duong und Thin Nguyen. „Domain Generalisation via Risk Distribution Matching“. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2024. http://dx.doi.org/10.1109/wacv57701.2024.00277.
Der volle Inhalt der QuelleLe, Hoang Son, Rini Akmeliawati und Gustavo Carneiro. „Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error“. In 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647203.
Der volle Inhalt der QuelleVu, Thuy-Trang, Shahram Khadivi, Dinh Phung und Gholamreza Haffari. „Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training“. In Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-acl.49.
Der volle Inhalt der QuelleArsenos, Anastasios, Dimitrios Kollias, Evangelos Petrongonas, Christos Skliros und Stefanos Kollias. „Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation“. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10448096.
Der volle Inhalt der QuelleAhmad, Rehan, Md Asif Jalal, Muhammad Umar Farooq, Anna Ollerenshaw und Thomas Hain. „Towards Domain Generalisation in ASR with Elitist Sampling and Ensemble Knowledge Distillation“. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095746.
Der volle Inhalt der QuelleChen, Qi, Bing Xue und Mengjie Zhang. „Generalisation and domain adaptation in GP with gradient descent for symbolic regression“. In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257017.
Der volle Inhalt der QuelleArora, Aseem, Shabbirhussain Bhaisaheb, Harshit Nigam, Manasi Patwardhan, Lovekesh Vig und Gautam Shroff. „Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting“. In Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.genbench-1.3.
Der volle Inhalt der QuelleKamenou, Eleni, Jesús Martínez Del Rincón, Paul Miller und Patricia Devlin-Hill. „A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification“. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00044.
Der volle Inhalt der QuelleAbeysinghe, Chamath, Chris Reid, Hamid Rezatofighi und Bernd Meyer. „Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/61.
Der volle Inhalt der QuellePascual, Rodrigo, Jean-Claude Golinval und Mario Razeto. „A Model Updating Method Based on the Concept of Frequency Shift Between Analytical and Experimental FRFs“. In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4146.
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