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Artykuły w czasopismach na temat "Domain generalisation"
Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales i Tao Xiang. "Deep Domain-Adversarial Image Generation for Domain Generalisation". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.
Pełny tekst źródłaSeemakurthy, Karthik, Charles Fox, Erchan Aptoula i 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.
Pełny tekst źródłaLe, Hoang Son, Rini Akmeliawati i 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.
Pełny tekst źródłaRoeper, P. "Generalisation of first-order logic to nonatomic domains". Journal of Symbolic Logic 50, nr 3 (wrzesień 1985): 815–38. http://dx.doi.org/10.2307/2274334.
Pełny tekst źródłaGomathi, R., i 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.
Pełny tekst źródłaMyers, Scott, i Jaye Padgett. "Domain generalisation in artificial language learning". Phonology 31, nr 3 (grudzień 2014): 399–433. http://dx.doi.org/10.1017/s0952675714000207.
Pełny tekst źródłaHYLAND, 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.
Pełny tekst źródłaRashid, Fariza, Ben Doyle, Soyeon Caren Han i Suranga Seneviratne. "Phishing URL detection generalisation using Unsupervised Domain Adaptation". Computer Networks 245 (maj 2024): 110398. http://dx.doi.org/10.1016/j.comnet.2024.110398.
Pełny tekst źródłaAnthony, Martin, Peter Bartlett, Yuval Ishai i John Shawe-Taylor. "Valid Generalisation from Approximate Interpolation". Combinatorics, Probability and Computing 5, nr 3 (wrzesień 1996): 191–214. http://dx.doi.org/10.1017/s096354830000198x.
Pełny tekst źródłaOrazov, 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.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaMcLean, David. "Improving generalisation in continuous data domains". Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283816.
Pełny tekst źródłaGuesdon, 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.
Pełny tekst źródłaResearch 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
Książki na temat "Domain generalisation"
Aubreville, Marc, David Zimmerer i Mattias Heinrich, red. 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.
Pełny tekst źródłaAubreville, Marc, David Zimmerer i 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.
Znajdź pełny tekst źródłaCzęści książek na temat "Domain generalisation"
Fick, Rutger H. J., Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit i Saima Ben Hadj. "Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection". W 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.
Pełny tekst źródłaLong, Xi, Ying Cheng, Xiao Mu, Lian Liu i Jingxin Liu. "Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge". W 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.
Pełny tekst źródłaAlmahfouz Nasser, Sahar, Nikhil Cherian Kurian i Amit Sethi. "Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers". W 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.
Pełny tekst źródłaWilm, Frauke, Christian Marzahl, Katharina Breininger i Marc Aubreville. "Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge". W 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.
Pełny tekst źródłaChung, Youjin, Jihoon Cho i Jinah Park. "Domain-Robust Mitotic Figure Detection with Style Transfer". W 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.
Pełny tekst źródłaYang, Sen, Feng Luo, Jun Zhang i Xiyue Wang. "Sk-Unet Model with Fourier Domain for Mitosis Detection". W 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.
Pełny tekst źródłaJahanifar, Mostafa, Adam Shepard, Neda Zamanitajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas i Nasir Rajpoot. "Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge". W 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.
Pełny tekst źródłaNateghi, Ramin, i Fattaneh Pourakpour. "Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images". W 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.
Pełny tekst źródłaLafarge, Maxime W., i Viktor H. Koelzer. "Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge". W 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.
Pełny tekst źródłaTan, Jeremy, Turkay Kart, Benjamin Hou, James Batten i Bernhard Kainz. "MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Domain generalisation"
Nguyen, Toan, Kien Do, Bao Duong i Thin Nguyen. "Domain Generalisation via Risk Distribution Matching". W 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2024. http://dx.doi.org/10.1109/wacv57701.2024.00277.
Pełny tekst źródłaLe, Hoang Son, Rini Akmeliawati i Gustavo Carneiro. "Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error". W 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647203.
Pełny tekst źródłaVu, Thuy-Trang, Shahram Khadivi, Dinh Phung i Gholamreza Haffari. "Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training". W 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.
Pełny tekst źródłaArsenos, Anastasios, Dimitrios Kollias, Evangelos Petrongonas, Christos Skliros i Stefanos Kollias. "Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation". W ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10448096.
Pełny tekst źródłaAhmad, Rehan, Md Asif Jalal, Muhammad Umar Farooq, Anna Ollerenshaw i Thomas Hain. "Towards Domain Generalisation in ASR with Elitist Sampling and Ensemble Knowledge Distillation". W ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095746.
Pełny tekst źródłaChen, Qi, Bing Xue i Mengjie Zhang. "Generalisation and domain adaptation in GP with gradient descent for symbolic regression". W 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257017.
Pełny tekst źródłaArora, Aseem, Shabbirhussain Bhaisaheb, Harshit Nigam, Manasi Patwardhan, Lovekesh Vig i Gautam Shroff. "Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting". W 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.
Pełny tekst źródłaKamenou, Eleni, Jesús Martínez Del Rincón, Paul Miller i Patricia Devlin-Hill. "A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification". W 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00044.
Pełny tekst źródłaAbeysinghe, Chamath, Chris Reid, Hamid Rezatofighi i Bernd Meyer. "Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking". W 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.
Pełny tekst źródłaPascual, Rodrigo, Jean-Claude Golinval i Mario Razeto. "A Model Updating Method Based on the Concept of Frequency Shift Between Analytical and Experimental FRFs". W 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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