Academic literature on the topic 'Label Shift'
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Journal articles on the topic "Label Shift"
Chow, C. W., and H. K. Tsang. "Orthogonal label switching using polarization-shift-keying payload and amplitude-shift-keying label." IEEE Photonics Technology Letters 17, no. 11 (November 2005): 2475–77. http://dx.doi.org/10.1109/lpt.2005.857590.
Full textWetterau, Lukas, Claas Abert, Dieter Suess, Manfred Albrecht, and Bernd Witzigmann. "Micromagnetic Simulations of Submicron Vortex Structures for the Detection of Superparamagnetic Labels." Sensors 20, no. 20 (October 15, 2020): 5819. http://dx.doi.org/10.3390/s20205819.
Full textYusheng, Cheng, Zhao Dawei, Zhan Wenfa, and Wang Yibin. "Multi-label learning of non-equilibrium labels completion with mean shift." Neurocomputing 321 (December 2018): 92–102. http://dx.doi.org/10.1016/j.neucom.2018.09.033.
Full textRezaei, Ashkan, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. "Robust Fairness Under Covariate Shift." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.
Full textZhang, Huayi, Lei Cao, Samuel Madden, and Elke Rundensteiner. "LANCET." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2154–66. http://dx.doi.org/10.14778/3476249.3476269.
Full textChen, Hongwei, Minghua Chen, Ciyuan Qiu, and Shizhong Xie. "Orthogonal polarization shift keying label rewriting method in an all-optical label switching network." Optics Letters 32, no. 9 (April 3, 2007): 1050. http://dx.doi.org/10.1364/ol.32.001050.
Full textPikramenos, George, Evaggelos Spyrou, and Stavros J. Perantonis. "Extending Partial Domain Adaptation Algorithms to the Open-Set Setting." Applied Sciences 12, no. 19 (October 6, 2022): 10052. http://dx.doi.org/10.3390/app121910052.
Full textChen, Jin, Xinxiao Wu, Yao Hu, and Jiebo Luo. "Spatial-temporal Causal Inference for Partial Image-to-video Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1027–35. http://dx.doi.org/10.1609/aaai.v35i2.16187.
Full textSmith, Lisa, Kimberly Arcand, Jeffrey Smith, Randall Smith, Jay Bookbinder, and Megan Watzke. "Examining perceptions of astronomy images across mobile platforms." Journal of Science Communication 13, no. 02 (March 25, 2014): A01. http://dx.doi.org/10.22323/2.13020201.
Full textMurza, Kimberly A., and Barbara J. Ehren. "Considering the Language Disorder Label Debate From a School Speech-Language Pathology Lens." Perspectives of the ASHA Special Interest Groups 5, no. 1 (February 21, 2020): 47–54. http://dx.doi.org/10.1044/2019_persp-19-00077.
Full textDissertations / Theses on the topic "Label Shift"
Fonseca, Eduardo. "Training sound event classifiers using different types of supervision." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673067.
Full textEl interés en el reconocimiento automático de eventos sonoros se ha incrementado en los últimos años, motivado por nuevas aplicaciones en campos como la asistencia médica, smart homes, o urbanismo. Al comienzo de esta tesis, la investigación en clasificación de eventos sonoros se centraba principalmente en aprendizaje supervisado usando datasets pequeños, a menudo anotados cuidadosamente con vocabularios limitados a dominios específicos (como el urbano o el doméstico). Sin embargo, tales datasets no permiten entrenar clasificadores capaces de reconocer los cientos de eventos sonoros que ocurren en nuestro entorno, como silbidos de kettle, sonidos de pájaros, coches pasando, o diferentes alarmas. Al mismo tiempo, websites como Freesound o YouTube albergan grandes cantidades de datos de sonido ambiental, que pueden ser útiles para entrenar clasificadores con un vocabulario más extenso, particularmente utilizando métodos de deep learning que requieren gran cantidad de datos. Para avanzar el estado del arte en la clasificación de eventos sonoros, esta tesis investiga varios aspectos de la creación de datasets, así como de aprendizaje supervisado y no supervisado para entrenar clasificadores de eventos sonoros con un vocabulario extenso, utilizando diferentes tipos de supervisión de manera novedosa y alternativa. En concreto, nos centramos en aprendizaje supervisado usando etiquetas sin ruido y con ruido, así como en aprendizaje de representaciones auto-supervisado a partir de datos no etiquetados. La primera parte de esta tesis se centra en la creación de FSD50K, un dataset con más de 100h de audio etiquetado manualmente usando 200 clases de eventos sonoros. Presentamos una descripción detallada del proceso de creación y una caracterización exhaustiva del dataset. Además, exploramos modificaciones arquitectónicas para aumentar la invariancia frente a desplazamientos en CNNs, mejorando la robustez frente a desplazamientos de tiempo/frecuencia en los espectrogramas de entrada. En la segunda parte, nos centramos en entrenar clasificadores de eventos sonoros usando etiquetas con ruido. Primero, proponemos un dataset que permite la investigación del ruido de etiquetas real. Después, exploramos métodos agnósticos a la arquitectura de red para mitigar el efecto del ruido en las etiquetas durante el entrenamiento, incluyendo técnicas de regularización, funciones de coste robustas al ruido, y estrategias para rechazar ejemplos etiquetados con ruido. Además, desarrollamos un método teacher-student para abordar el problema de las etiquetas ausentes en datasets de eventos sonoros. En la tercera parte, proponemos algoritmos para aprender representaciones de audio a partir de datos sin etiquetar. En particular, desarrollamos métodos de aprendizaje contrastivos auto-supervisados, donde las representaciones se aprenden comparando pares de ejemplos calculados a través de métodos de aumento de datos y separación automática de sonido. Finalmente, reportamos sobre la organización de dos DCASE Challenge Tasks para el tageado automático de audio a partir de etiquetas ruidosas. Mediante la propuesta de datasets, así como de métodos de vanguardia y representaciones de audio, esta tesis contribuye al avance de la investigación abierta sobre eventos sonoros y a la transición del aprendizaje supervisado tradicional utilizando etiquetas sin ruido a otras estrategias de aprendizaje menos dependientes de costosos esfuerzos de anotación.
Caye, Daudt Rodrigo. "Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT033.
Full textThe analysis of satellite and aerial Earth observation images allows us to obtain precise information over large areas. A multitemporal analysis of such images is necessary to understand the evolution of such areas. In this thesis, convolutional neural networks are used to detect and understand changes using remote sensing images from various sources in supervised and weakly supervised settings. Siamese architectures are used to compare coregistered image pairs and to identify changed pixels. The proposed method is then extended into a multitask network architecture that is used to detect changes and perform land cover mapping simultaneously, which permits a semantic understanding of the detected changes. Then, classification filtering and a novel guided anisotropic diffusion algorithm are used to reduce the effect of biased label noise, which is a concern for automatically generated large-scale datasets. Weakly supervised learning is also achieved to perform pixel-level change detection using only image-level supervision through the usage of class activation maps and a novel spatial attention layer. Finally, a domain adaptation method based on adversarial training is proposed, which succeeds in projecting images from different domains into a common latent space where a given task can be performed. This method is tested not only for domain adaptation for change detection, but also for image classification and semantic segmentation, which proves its versatility
Books on the topic "Label Shift"
Cinquegrani, Alessandro, Francesca Pangallo, and Federico Rigamonti. Romance e Shoah Pratiche di narrazione sulla tragedia indicibile. Venice: Fondazione Università Ca’ Foscari, 2021. http://dx.doi.org/10.30687/978-88-6969-492-9.
Full textSands, Bonny. The Challenge of Documenting Africa’s Least-Known Languages. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190256340.003.0002.
Full textDay, Gloria. Weight of Labels: A Poetic Display of an Internal Shift. Lulu Press, Inc., 2018.
Find full textKahlos, Maijastina. Religious Dissent in Late Antiquity, 350-450. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190067250.001.0001.
Full textFigone, Albert J. Do No Evil, See No Evil, and Hear No Evil. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037283.003.0004.
Full textHaig, Geoffrey. Deconstructing Iranian Ergativity. Edited by Jessica Coon, Diane Massam, and Lisa Demena Travis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198739371.013.20.
Full textLundén, Elizabeth Castaldo. Fashion on the Red Carpet. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474461801.001.0001.
Full textBook chapters on the topic "Label Shift"
Hwang, Sehyun, Sohyun Lee, Sungyeon Kim, Jungseul Ok, and Suha Kwak. "Combating Label Distribution Shift for Active Domain Adaptation." In Lecture Notes in Computer Science, 549–66. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19827-4_32.
Full textRodriguez-Furlan, Cecilia, and Glenn R. Hicks. "Label-Free and Confirmation Using Thermal Stability Shift Assays." In Methods in Molecular Biology, 163–73. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0954-5_14.
Full textMa, Wenao, Cheng Chen, Shuang Zheng, Jing Qin, Huimao Zhang, and Qi Dou. "Test-Time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift." In Lecture Notes in Computer Science, 313–23. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16437-8_30.
Full textDoležel, Michal. "Defining TestOps: Collaborative Behaviors and Technology-Driven Workflows Seen as Enablers of Effective Software Testing in DevOps." In Agile Processes in Software Engineering and Extreme Programming – Workshops, 253–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58858-8_26.
Full textKim, Dongwan, Yi-Hsuan Tsai, Yumin Suh, Masoud Faraki, Sparsh Garg, Manmohan Chandraker, and Bohyung Han. "Learning Semantic Segmentation from Multiple Datasets with Label Shifts." In Lecture Notes in Computer Science, 20–36. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19815-1_2.
Full textFilbrandt, Gregory, Konstantinos Kamnitsas, David Bernstein, Alexandra Taylor, and Ben Glocker. "Learning from Partially Overlapping Labels: Image Segmentation Under Annotation Shift." In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 123–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4_12.
Full textManakov, Ilja, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, and Volker Tresp. "Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation." In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, 3–10. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1_1.
Full textCorens, Liesbeth. "Introduction." In Confessional Mobility and English Catholics in Counter-Reformation Europe, 1–20. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812432.003.0008.
Full textLépinard, Éléonore. "Feminist Whiteness." In Feminist Trouble, 81–126. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190077150.003.0004.
Full textTreacy, Corbin. "Writing in the Aftermath of Two Wars: Algerian Modernism and the Génération ’88." In Algeria. Liverpool University Press, 2017. http://dx.doi.org/10.5949/liverpool/9781786940216.003.0007.
Full textConference papers on the topic "Label Shift"
Liu, Xiaofeng, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, and Jonghye Woo. "Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/122.
Full textShi, Haochen, Siliang Tang, Xiaotao Gu, Bo Chen, Zhigang Chen, Jian Shao, and Xiang Ren. "Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/539.
Full textWen, Jun, Nenggan Zheng, Junsong Yuan, Zhefeng Gong, and Changyou Chen. "Bayesian Uncertainty Matching for Unsupervised Domain Adaptation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/534.
Full textChow, C. W., and H. K. Tsang. "Optical packet labeling using polarization shift keying (PoISK) label and amplitude shift keying (ASK) payload." In 2005 Optical Fiber Communications Conference Technical Digest. IEEE, 2005. http://dx.doi.org/10.1109/ofc.2005.192564.
Full textZhang, Qinming, Luyan Liu, Kai Ma, Cheng Zhuo, and Yefeng Zheng. "Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/146.
Full textLiu, Yahao, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, and Wen Li. "Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00691.
Full textSordillo, Laura A., Peter P. Sordillo, and R. R. Alfano. "Label-free pathological evaluation of grade 3 cancer using Stokes shift spectroscopy." In SPIE BiOS, edited by Robert R. Alfano and Stavros G. Demos. SPIE, 2016. http://dx.doi.org/10.1117/12.2214327.
Full textGawlikowski, Jakob, Sudipan Saha, Julia Niebling, and Xiao Xiang Zhu. "Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884880.
Full textRosenzweig, Julia, Eduardo Brito, Hans-Ulrich Kobialka, Maram Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, et al. "Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis." In 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops). IEEE, 2021. http://dx.doi.org/10.1109/ivworkshops54471.2021.9669248.
Full textLiu, Xiaofeng, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. C. Jay Kuo, Georges El Fakhri, and Jonghye Woo. "Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01020.
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