Auswahl der wissenschaftlichen Literatur zum Thema „Out-of-distribution generalization“
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Zeitschriftenartikel zum Thema "Out-of-distribution generalization"
Ye, Nanyang, Lin Zhu, Jia Wang, Zhaoyu Zeng, Jiayao Shao, Chensheng Peng, Bikang Pan, Kaican Li und Jun Zhu. „Certifiable Out-of-Distribution Generalization“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 9 (26.06.2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.
Der volle Inhalt der QuelleYuan, Lingxiao, Harold S. Park und Emma Lejeune. „Towards out of distribution generalization for problems in mechanics“. Computer Methods in Applied Mechanics and Engineering 400 (Oktober 2022): 115569. http://dx.doi.org/10.1016/j.cma.2022.115569.
Der volle Inhalt der QuelleLiu, Anji, Hongming Xu, Guy Van den Broeck und Yitao Liang. „Out-of-Distribution Generalization by Neural-Symbolic Joint Training“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 10 (26.06.2023): 12252–59. http://dx.doi.org/10.1609/aaai.v37i10.26444.
Der volle Inhalt der QuelleYu, Yemin, Luotian Yuan, Ying Wei, Hanyu Gao, Fei Wu, Zhihua Wang und Xinhai Ye. „RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 1 (24.03.2024): 374–82. http://dx.doi.org/10.1609/aaai.v38i1.27791.
Der volle Inhalt der QuelleZhu, Lin, Xinbing Wang, Chenghu Zhou und Nanyang Ye. „Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 9 (26.06.2023): 11461–69. http://dx.doi.org/10.1609/aaai.v37i9.26355.
Der volle Inhalt der QuelleLavda, Frantzeska, und Alexandros Kalousis. „Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation“. Entropy 25, Nr. 12 (14.12.2023): 1659. http://dx.doi.org/10.3390/e25121659.
Der volle Inhalt der QuelleSu, Hang, und Wei Wang. „An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment“. Mathematics 12, Nr. 1 (26.12.2023): 85. http://dx.doi.org/10.3390/math12010085.
Der volle Inhalt der QuelleCao, Linfeng, Aofan Jiang, Wei Li, Huaying Wu und Nanyang Ye. „OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 1 (28.06.2022): 158–66. http://dx.doi.org/10.1609/aaai.v36i1.19890.
Der volle Inhalt der QuelleDeng, Bin, und Kui Jia. „Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization“. Entropy 25, Nr. 2 (18.01.2023): 193. http://dx.doi.org/10.3390/e25020193.
Der volle Inhalt der QuelleAshok, Arjun, Chaitanya Devaguptapu und Vineeth N. Balasubramanian. „Learning Modular Structures That Generalize Out-of-Distribution (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 11 (28.06.2022): 12905–6. http://dx.doi.org/10.1609/aaai.v36i11.21589.
Der volle Inhalt der QuelleDissertationen zum Thema "Out-of-distribution generalization"
Kirchmeyer, Matthieu. „Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS080.
Der volle Inhalt der QuelleDeep learning has emerged as a powerful approach for modelling static data like images and more recently for modelling dynamical systems like those underlying times series, videos or physical phenomena. Yet, neural networks were observed to not generalize well outside the training distribution, in other words out-of-distribution. This lack of generalization limits the deployment of deep learning in autonomous systems or online production pipelines, which are faced with constantly evolving data. In this thesis, we design new strategies for out-of-distribution generalization. These strategies handle the specific challenges posed by two main application tasks, classification of static data and spatiotemporal dynamics forecasting. The first two parts of this thesis consider the classification problem. We first investigate how we can efficiently leverage some observed training data from a target domain for adaptation. We then explore how to generalize to unobserved domains without access to such data. The last part of this thesis handles various generalization problems specific to spatiotemporal forecasting
Bücher zum Thema "Out-of-distribution generalization"
Zabrodin, Anton. Financial applications of random matrix theory: a short review. Herausgegeben von Gernot Akemann, Jinho Baik und Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.
Der volle Inhalt der QuelleJames, Philip. The Biology of Urban Environments. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198827238.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Out-of-distribution generalization"
Chen, Zining, Weiqiu Wang, Zhicheng Zhao, Aidong Men und Hong Chen. „Bag of Tricks for Out-of-Distribution Generalization“. In Lecture Notes in Computer Science, 465–76. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_31.
Der volle Inhalt der QuelleMoruzzi, Caterina. „Toward Out-of-Distribution Generalization Through Inductive Biases“. In Studies in Applied Philosophy, Epistemology and Rational Ethics, 57–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09153-7_5.
Der volle Inhalt der QuelleLi, Dongqi, Zhu Teng, Qirui Li und Ziyin Wang. „Sharpness-Aware Minimization for Out-of-Distribution Generalization“. In Communications in Computer and Information Science, 555–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8126-7_43.
Der volle Inhalt der QuelleWang, Fawu, Kang Zhang, Zhengyu Liu, Xia Yuan und Chunxia Zhao. „Deep Relevant Feature Focusing for Out-of-Distribution Generalization“. In Pattern Recognition and Computer Vision, 245–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_19.
Der volle Inhalt der QuelleWang, Yuqing, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng und Lei Meng. „Meta-Causal Feature Learning for Out-of-Distribution Generalization“. In Lecture Notes in Computer Science, 530–45. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_36.
Der volle Inhalt der QuelleYu, Haoran, Baodi Liu, Yingjie Wang, Kai Zhang, Dapeng Tao und Weifeng Liu. „A Stable Vision Transformer for Out-of-Distribution Generalization“. In Pattern Recognition and Computer Vision, 328–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_27.
Der volle Inhalt der QuelleZhang, Xingxuan, Yue He, Tan Wang, Jiaxin Qi, Han Yu, Zimu Wang, Jie Peng et al. „NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges“. In Lecture Notes in Computer Science, 433–50. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_29.
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 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 QuelleWang, Jiahao, Hao Wang, Zhuojun Dong, Hua Yang, Yuting Yang, Qianyue Bao, Fang Liu und LiCheng Jiao. „A Three-Stage Model Fusion Method for Out-of-Distribution Generalization“. In Lecture Notes in Computer Science, 488–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_33.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Out-of-distribution generalization"
Wang, Fawu, Ruizhe Li, Kang Zhang, Xia Yuan und Chunxia Zhao. „Data Distribution Transfer for Out Of Distribution Generalization“. In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022. http://dx.doi.org/10.1109/mmsp55362.2022.9949199.
Der volle Inhalt der QuelleWang, Ruoyu, Mingyang Yi, Zhitang Chen und Shengyu Zhu. „Out-of-distribution Generalization with Causal Invariant Transformations“. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00047.
Der volle Inhalt der QuelleDeng, Xun, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He und Yong Liao. „Counterfactual Active Learning for Out-of-Distribution Generalization“. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.636.
Der volle Inhalt der QuelleZhang, Xingxuan, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He und Zheyan Shen. „Deep Stable Learning for Out-Of-Distribution Generalization“. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00533.
Der volle Inhalt der QuelleWu, Qitian, Fan Nie, Chenxiao Yang, Tianyi Bao und Junchi Yan. „Graph Out-of-Distribution Generalization via Causal Intervention“. In WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589334.3645604.
Der volle Inhalt der QuelleKamani, Mohammad Mahdi, Sadegh Farhang, Mehrdad Mahdavi und James Z. Wang. „Targeted Data-driven Regularization for Out-of-Distribution Generalization“. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403131.
Der volle Inhalt der QuelleWang, Xin, Peng Cui und Wenwu Zhu. „Out-of-distribution Generalization and Its Applications for Multimedia“. In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3478876.
Der volle Inhalt der QuelleMiao, Qiaowei, Junkun Yuan, Shengyu Zhang, Fei Wu und Kun Kuang. „Domaindiff: Boost out-of-Distribution Generalization with Synthetic Data“. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446788.
Der volle Inhalt der QuelleBai, Haoyue, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S. H. Gary Chan und Zhenguo Li. „NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization“. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00821.
Der volle Inhalt der QuelleSun, Yihong, Adam Kortylewski und Alan Yuille. „Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model“. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00128.
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