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Статті в журналах з теми "Fully- and weakly-Supervised learning"
Cuypers, Suzanna, Maarten Bassier, and Maarten Vergauwen. "Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation." Sensors 21, no. 16 (August 11, 2021): 5428. http://dx.doi.org/10.3390/s21165428.
Повний текст джерелаWang, Ning, Jiajun Deng, and Mingbo Jia. "Cycle-Consistency Learning for Captioning and Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 5535–43. http://dx.doi.org/10.1609/aaai.v38i6.28363.
Повний текст джерелаWang, Guangyao. "A Study of Object Detection Based on Weakly Supervised Learning." International Journal of Computer Science and Information Technology 2, no. 1 (March 25, 2024): 476–78. http://dx.doi.org/10.62051/ijcsit.v2n1.50.
Повний текст джерелаAdke, Shrinidhi, Changying Li, Khaled M. Rasheed, and Frederick W. Maier. "Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery." Sensors 22, no. 10 (May 12, 2022): 3688. http://dx.doi.org/10.3390/s22103688.
Повний текст джерелаNi, Ansong, Pengcheng Yin, and Graham Neubig. "Merging Weak and Active Supervision for Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8536–43. http://dx.doi.org/10.1609/aaai.v34i05.6375.
Повний текст джерелаColin, Aurélien, Ronan Fablet, Pierre Tandeo, Romain Husson, Charles Peureux, Nicolas Longépé, and Alexis Mouche. "Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning." Remote Sensing 14, no. 4 (February 11, 2022): 851. http://dx.doi.org/10.3390/rs14040851.
Повний текст джерелаCai, Tingting, Hongping Yan, Kun Ding, Yan Zhang, and Yueyue Zhou. "WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation." Applied Sciences 14, no. 12 (June 8, 2024): 5007. http://dx.doi.org/10.3390/app14125007.
Повний текст джерелаHong, Yining, Qing Li, Daniel Ciao, Siyuan Huang, and Song-Chun Zhu. "Learning by Fixing: Solving Math Word Problems with Weak Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 4959–67. http://dx.doi.org/10.1609/aaai.v35i6.16629.
Повний текст джерелаChen, Shaolong, and Zhiyong Zhang. "A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning." Sensors 24, no. 12 (June 16, 2024): 3893. http://dx.doi.org/10.3390/s24123893.
Повний текст джерелаZhang, Yachao, Zonghao Li, Yuan Xie, Yanyun Qu, Cuihua Li, and Tao Mei. "Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3421–29. http://dx.doi.org/10.1609/aaai.v35i4.16455.
Повний текст джерелаДисертації з теми "Fully- and weakly-Supervised learning"
Ma, Qixiang. "Deep learning based segmentation and detection of aorta structures in CT images involving fully and weakly supervised learning." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS029.
Повний текст джерелаEndovascular aneurysm repair (EVAR) and transcatheter aortic valve implantation (TAVI) are endovascular interventions where preoperative CT image analysis is a prerequisite for planning and navigation guidance. In the case of EVAR procedures, the focus is specifically on the challenging issue of aortic segmentation in non-contrast-enhanced CT (NCCT) imaging, which remains unresolved. For TAVI procedures, attention is directed toward detecting anatomical landmarks to predict the risk of complications and select the bioprosthesis. To address these challenges, we propose automatic methods based on deep learning (DL). Firstly, a fully-supervised model based on 2D-3D features fusion is proposed for vascular segmentation in NCCTs. Subsequently, a weakly-supervised framework based on Gaussian pseudo labels is considered to reduce and facilitate manual annotation during the training phase. Finally, hybrid weakly- and fully-supervised methods are proposed to extend segmentation to more complex vascular structures beyond the abdominal aorta. When it comes to aortic valve in cardiac CT scans, a two-stage fully-supervised DL method is proposed for landmarks detection. The results contribute to enhancing preoperative imaging and the patient's digital model for computer-assisted endovascular interventions
Hlynur, Davíð Hlynsson. "Predicting expert moves in the game of Othello using fully convolutional neural networks." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210914.
Повний текст джерелаNoggrann funktionsteknik är en viktig faktor för artificiell intelligens för spel. I dennaavhandling undersöker jag fördelarna med att delegera teknikarbetet till modellen i ställetför de funktioner, som använder brädspelet Othello som en fallstudie. Konvolutionellaneurala nätverk av varierande djup är utbildade att spela på ett mänskligt sätt genom attlära sig att förutsäga handlingar från turneringar. Mitt främsta resultat är att ett nätverkkan utbildas för att uppnå 57,4% prediktionsnoggrannhet på en testuppsättning, vilketöverträffar tidigare toppmoderna i den här uppgiften. Noggrannheten ökar till 58.3% genomatt lägga till flera vanliga handgjorda funktioner som inmatning till nätverket, tillkostnaden för mer än hälften så mycket beräknatid.
Durand, Thibaut. "Weakly supervised learning for visual recognition." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066142/document.
Повний текст джерелаThis thesis studies the problem of classification of images, where the goal is to predict if a semantic category is present in the image, based on its visual content. To analyze complex scenes, it is important to learn localized representations. To limit the cost of annotation during training, we have focused on weakly supervised learning approaches. In this thesis, we propose several models that simultaneously classify and localize objects, using only global labels during training. The weak supervision significantly reduces the cost of full annotation, but it makes learning more challenging. The key issue is how to aggregate local scores - e.g. regions - into global score - e.g. image. The main contribution of this thesis is the design of new pooling functions for weakly supervised learning. In particular, we propose a “max + min” pooling function, which unifies many pooling functions. We describe how to use this pooling in the Latent Structured SVM framework as well as in convolutional networks. To solve the optimization problems, we present several solvers, some of which allow to optimize a ranking metric such as Average Precision. We experimentally show the interest of our models with respect to state-of-the-art methods, on ten standard image classification datasets, including the large-scale dataset ImageNet
Durand, Thibaut. "Weakly supervised learning for visual recognition." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066142.
Повний текст джерелаThis thesis studies the problem of classification of images, where the goal is to predict if a semantic category is present in the image, based on its visual content. To analyze complex scenes, it is important to learn localized representations. To limit the cost of annotation during training, we have focused on weakly supervised learning approaches. In this thesis, we propose several models that simultaneously classify and localize objects, using only global labels during training. The weak supervision significantly reduces the cost of full annotation, but it makes learning more challenging. The key issue is how to aggregate local scores - e.g. regions - into global score - e.g. image. The main contribution of this thesis is the design of new pooling functions for weakly supervised learning. In particular, we propose a “max + min” pooling function, which unifies many pooling functions. We describe how to use this pooling in the Latent Structured SVM framework as well as in convolutional networks. To solve the optimization problems, we present several solvers, some of which allow to optimize a ranking metric such as Average Precision. We experimentally show the interest of our models with respect to state-of-the-art methods, on ten standard image classification datasets, including the large-scale dataset ImageNet
Raisi, Elaheh. "Weakly Supervised Machine Learning for Cyberbullying Detection." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89100.
Повний текст джерелаDoctor of Philosophy
Social media has become an inevitable part of individuals social and business lives. Its benefits, however, come with various negative consequences such as online harassment, cyberbullying, hate speech, and online trolling especially among the younger population. According to the American Academy of Child and Adolescent Psychiatry,1 victims of bullying can suffer interference to social and emotional development and even be drawn to extreme behavior such as attempted suicide. Any widespread bullying enabled by technology represents a serious social health threat. In this research, we develop automated, data-driven methods for harassment-based cyberbullying detection. The availability of tools such as these can enable technologies that reduce the harm and toxicity created by these detrimental behaviors. Our general framework is based on consistency of two detectors that co-train one another. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. When designing the general framework, we address three tasks: First, we use machine learning with weak supervision, which significantly alleviates the need for human experts to perform tedious data annotation. Second, we incorporate the efficacy of distributed representations of words and nodes such as deep, nonlinear models in the framework to improve the predictive power of models. Finally, we decrease the sensitivity of the framework to language describing particular social groups including race, gender, religion, and sexual orientation. This research represents important steps toward improving technological capability for automatic cyberbullying detection.
Hanwell, David. "Weakly supervised learning of visual semantic attributes." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.
Повний текст джерелаKumar, M. Pawan. "Weakly Supervised Learning for Structured Output Prediction." Habilitation à diriger des recherches, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00943602.
Повний текст джерелаNodet, Pierre. "Biquality learning : from weakly supervised learning to distribution shifts." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG030.
Повний текст джерелаThe field of Learning with weak supervision is called Weakly Supervised Learning and aggregates a variety of situations where the collected ground truth is imperfect. The collected labels may suffer from bad quality, non-adaptability, or insufficient quantity. In this report, we propose a novel taxonomy of Weakly Supervised Learning as a continuous cube called the Weak Supervision Cube that encompasses all of the weaknesses of supervision. To design algorithms capable of handling any weak supervisions, we suppose the availability of a small trusted dataset, without bias and corruption, in addition to the potentially corrupted dataset. The trusted dataset allows the definition of a generic learning framework named Biquality Learning. We review the state-of-the-art of these algorithms that assumed the availability of a small trusted dataset. Under this framework, we propose an algorithm based on Importance Reweighting for Biquality Learning (IRBL). This classifier-agnostic approach is based on the empirical estimation of the Radon-Nikodym derivative (RND), to build a risk-consistent estimator on reweighted untrusted data. Then we extend the proposed framework to dataset shifts. Dataset shifts happen when the data distribution observed at training time is different from what is expected from the data distribution at testing time. So we propose an improved version of IRBL named IRBL2, capable of handling such dataset shifts. Additionally, we propose another algorithm named KPDR based on the same theory but focused on covariate shift instead of the label noise formulation. To diffuse and democratize the Biquality Learning Framework, we release an open-source Python library à la Scikit-Learn for Biquality Learning named biquality-learn
Ruiz, Ovejero Adrià. "Weakly-supervised learning for automatic facial behaviour analysis." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457708.
Повний текст джерелаAquesta tesi doctoral se centra en el problema de l'Anàlisi Automàtic del Comportament Facial, on l'objectiu és desenvolupar sistemes autònoms capaços de reconèixer i entendre les expressions facials humanes. Donada la quantitat d'informació que es pot extreure d'aquestes expressions, sistemes d'aquest tipus tenen multitud d'aplicacions en camps com la Interacció Home-Màquina, el Marketing o l'Assistència Clínica. Per aquesta raó, investigadors en Visió per Computador i Aprenentatge Automàtic han destinat molts esforços en les últimes dècades per tal d'aconseguir avenços en aquest sentit. Malgrat això, la majoria de problemes relacionats amb l'anàlisi automàtic d'expressions facials encara estan lluny de ser conisderats com a resolts. En aquest context, aquesta tesi està motivada pel fet que la majoria de mètodes proposats fins ara han seguit el paradigma d'aprenentatge supervisat, on els models són entrenats mitjançant dades anotades explícitament en funció del problema a resoldre. Desafortunadament, aquesta estratègia té grans limitacions donat que l'anotació d'expressions en bases de dades és una tasca molt costosa i lenta. Per tal d'afrontar aquest repte, aquesta tesi proposa encarar l'Anàlisi Automàtic del Comportament Facial mitjançant el paradigma d'aprenentatge dèbilment supervisat. A diferència del cas anterior, aquests models poden ser entrenats utilitzant etiquetes que són fàcils d'anotar però que només donen informació parcial sobre la tasca que es vol aprendre. Seguint aquesta idea, desenvolupem un conjunt de mètodes per tal de resoldre problemes típics en el camp com el reconeixement d' "Action Units", l'Estimació d'Intensitat d'Expressions Facials o l'Anàlisi Emocional. Els resultats obtinguts avaluant els mètodes presentats en aquestes tasques, demostren que l'aprenentatge dèbilment supervisat pot ser una solució per tal de reduir l'esforç d'anotació en l'Anàlisi Automàtic del Comportament Facial. De la mateixa manera, aquests mètodes es mostren útils a l'hora de facilitar el procés d'etiquetatge de bases de dades creades per aquest propòsit.
Siva, Parthipan. "Automatic annotation for weakly supervised learning of detectors." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/3359.
Повний текст джерелаКниги з теми "Fully- and weakly-Supervised learning"
Munro, Paul. Self-supervised learning of concepts by single units and "weakly local" representations. Pittsburgh, PA: School of Library and Information Science, University of Pittsburgh, 1988.
Знайти повний текст джерелаRobert, Tibshirani, and Friedman J. H, eds. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. New York: Springer, 2001.
Знайти повний текст джерелаMunro, Paul. Self-supervised learning of concepts by single units and "weakly local" representations. School of Library and Information Science, University of Pittsburgh, 1988.
Знайти повний текст джерелаUnsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography. [New York, N.Y.?]: [publisher not identified], 2019.
Знайти повний текст джерелаЧастини книг з теми "Fully- and weakly-Supervised learning"
Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Relaxation Networks." In Supervised Learning with Complex-valued Neural Networks, 73–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_4.
Повний текст джерелаTran, Manuel, Sophia J. Wagner, Melanie Boxberg, and Tingying Peng. "S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning." In Lecture Notes in Computer Science, 99–108. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_10.
Повний текст джерелаSuresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Multi Layer Perceptron Networks." In Supervised Learning with Complex-valued Neural Networks, 31–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_2.
Повний текст джерелаTrivedi, Devharsh, Aymen Boudguiga, and Nikos Triandopoulos. "SigML: Supervised Log Anomaly with Fully Homomorphic Encryption." In Cyber Security, Cryptology, and Machine Learning, 372–88. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34671-2_26.
Повний текст джерелаBaur, Christoph, Shadi Albarqouni, and Nassir Navab. "Semi-supervised Deep Learning for Fully Convolutional Networks." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 311–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_36.
Повний текст джерелаSuresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "A Fully Complex-valued Radial Basis Function Network and Its Learning Algorithm." In Supervised Learning with Complex-valued Neural Networks, 49–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_3.
Повний текст джерелаMoukafih, Youness, Abdelghani Ghanem, Karima Abidi, Nada Sbihi, Mounir Ghogho, and Kamel Smaili. "SimSCL: A Simple Fully-Supervised Contrastive Learning Framework for Text Representation." In Lecture Notes in Computer Science, 728–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_59.
Повний текст джерелаSaha, Pramit, Divyanshu Mishra, and J. Alison Noble. "Rethinking Semi-Supervised Federated Learning: How to Co-train Fully-Labeled and Fully-Unlabeled Client Imaging Data." In Lecture Notes in Computer Science, 414–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_39.
Повний текст джерелаTorresani, Lorenzo. "Weakly Supervised Learning." In Computer Vision, 883–85. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_308.
Повний текст джерелаMehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science, 1–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.
Повний текст джерелаТези доповідей конференцій з теми "Fully- and weakly-Supervised learning"
Gliga, Lavinius-Ioan, Jeroen Zegers, Carlos Tiana Gomez, and Pieter Bovijn. "Self, Semi and Fully Supervised Learning for Autoencoders using Ternary Classification." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 33–40. IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10627326.
Повний текст джерелаTang, Yi, Yi Gao, Yong-gang Luo, Ju-Cheng Yang, Miao Xu, and Min-Ling Zhang. "Unlearning from Weakly Supervised Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/553.
Повний текст джерелаSoltanian-Zadeh, Somayyeh, Kazuhiro Kurokawa, Zhuolin Liu, Daniel X. Hammer, Donald T. Miller, and Sina Farsiu. "Fully automatic quantification of individual ganglion cells from AO-OCT volumes via weakly supervised learning." In Ophthalmic Technologies XXX, edited by Fabrice Manns, Per G. Söderberg, and Arthur Ho. SPIE, 2020. http://dx.doi.org/10.1117/12.2543964.
Повний текст джерелаLiu, Lu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph." 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.2019/418.
Повний текст джерелаWang, Jiapeng, Tianwei Wang, Guozhi Tang, Lianwen Jin, Weihong Ma, Kai Ding, and Yichao Huang. "Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences." 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.2021/150.
Повний текст джерелаWu, Yuanchen, Xiaoqiang Li, Songmin Dai, Jide Li, Tong Liu, and Shaorong Xie. "Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation." 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.2023/171.
Повний текст джерелаDai, Zhigang, Bolun Cai, and Junying Chen. "UniMoCo: Unsupervised, Semi-Supervised and Fully-Supervised Visual Representation Learning." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945500.
Повний текст джерелаWang, Yifeng, and Yi Zhao. "Scale and Direction Guided GAN for Inertial Sensor Signal Enhancement." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/567.
Повний текст джерелаWang, Guanchun, Xiangrong Zhang, Zelin Peng, Xu Tang, Huiyu Zhou, and Licheng Jiao. "Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/192.
Повний текст джерелаPagé Fortin, Mathieu, and Brahim Chaib-draa. "Continual Semantic Segmentation Leveraging Image-level Labels and Rehearsal." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/177.
Повний текст джерелаЗвіти організацій з теми "Fully- and weakly-Supervised learning"
Nguyen, Minh H., Lorenzo Torresani, Fernando de la Torre, and Carsten Rother. Weakly Supervised Discriminative Localization and Classification: A Joint Learning Process. Fort Belvoir, VA: Defense Technical Information Center, July 2009. http://dx.doi.org/10.21236/ada507101.
Повний текст джерела