Artículos de revistas sobre el tema "Learning with noisy labels"
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Xie, Ming-Kun y Sheng-Jun Huang. "Partial Multi-Label Learning with Noisy Label Identification". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6454–61. http://dx.doi.org/10.1609/aaai.v34i04.6117.
Texto completoChen, Mingcai, Hao Cheng, Yuntao Du, Ming Xu, Wenyu Jiang y Chongjun Wang. "Two Wrongs Don’t Make a Right: Combating Confirmation Bias in Learning with Label Noise". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 14765–73. http://dx.doi.org/10.1609/aaai.v37i12.26725.
Texto completoLi, Hui, Zhaodong Niu, Quan Sun y Yabo Li. "Co-Correcting: Combat Noisy Labels in Space Debris Detection". Remote Sensing 14, n.º 20 (21 de octubre de 2022): 5261. http://dx.doi.org/10.3390/rs14205261.
Texto completoTang, Xinyu, Milad Nasr, Saeed Mahloujifar, Virat Shejwalkar, Liwei Song, Amir Houmansadr y Prateek Mittal. "Machine Learning with Differentially Private Labels: Mechanisms and Frameworks". Proceedings on Privacy Enhancing Technologies 2022, n.º 4 (octubre de 2022): 332–50. http://dx.doi.org/10.56553/popets-2022-0112.
Texto completoWu, Yichen, Jun Shu, Qi Xie, Qian Zhao y Deyu Meng. "Learning to Purify Noisy Labels via Meta Soft Label Corrector". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 10388–96. http://dx.doi.org/10.1609/aaai.v35i12.17244.
Texto completoZheng, Guoqing, Ahmed Hassan Awadallah y Susan Dumais. "Meta Label Correction for Noisy Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 11053–61. http://dx.doi.org/10.1609/aaai.v35i12.17319.
Texto completoShi, Jialin, Chenyi Guo y Ji Wu. "A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels". Future Internet 14, n.º 2 (26 de enero de 2022): 41. http://dx.doi.org/10.3390/fi14020041.
Texto completoNorthcutt, Curtis, Lu Jiang y Isaac Chuang. "Confident Learning: Estimating Uncertainty in Dataset Labels". Journal of Artificial Intelligence Research 70 (14 de abril de 2021): 1373–411. http://dx.doi.org/10.1613/jair.1.12125.
Texto completoSilva, Amila, Ling Luo, Shanika Karunasekera y Christopher Leckie. "Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junio de 2022): 8315–23. http://dx.doi.org/10.1609/aaai.v36i8.20806.
Texto completoYan, Xuguo, Xuhui Xia, Lei Wang y Zelin Zhang. "A Progressive Deep Neural Network Training Method for Image Classification with Noisy Labels". Applied Sciences 12, n.º 24 (12 de diciembre de 2022): 12754. http://dx.doi.org/10.3390/app122412754.
Texto completoLi, Shikun, Shiming Ge, Yingying Hua, Chunhui Zhang, Hao Wen, Tengfei Liu y Weiqiang Wang. "Coupled-View Deep Classifier Learning from Multiple Noisy Annotators". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4667–74. http://dx.doi.org/10.1609/aaai.v34i04.5898.
Texto completoChen, Pengfei, Junjie Ye, Guangyong Chen, Jingwei Zhao y Pheng-Ann Heng. "Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11451–61. http://dx.doi.org/10.1609/aaai.v35i13.17364.
Texto completoYi, Rumeng, Dayan Guan, Yaping Huang y Shijian Lu. "Class-Independent Regularization for Learning with Noisy Labels". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junio de 2023): 3276–84. http://dx.doi.org/10.1609/aaai.v37i3.25434.
Texto completoGuo, Biyang, Songqiao Han, Xiao Han, Hailiang Huang y Ting Lu. "Label Confusion Learning to Enhance Text Classification Models". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 14 (18 de mayo de 2021): 12929–36. http://dx.doi.org/10.1609/aaai.v35i14.17529.
Texto completoNushi, Besmira, Adish Singla, Andreas Krause y Donald Kossmann. "Learning and Feature Selection under Budget Constraints in Crowdsourcing". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (21 de septiembre de 2016): 159–68. http://dx.doi.org/10.1609/hcomp.v4i1.13278.
Texto completoKo, Jongwoo, Bongsoo Yi y Se-Young Yun. "A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 8325–33. http://dx.doi.org/10.1609/aaai.v37i7.26004.
Texto completoZhao, Tianna, Yuanjian Zhang y Witold Pedrycz. "Robust Multi-Label Classification with Enhanced Global and Local Label Correlation". Mathematics 10, n.º 11 (30 de mayo de 2022): 1871. http://dx.doi.org/10.3390/math10111871.
Texto completoNie, Binling y Chenyang Li. "Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction". Applied Sciences 12, n.º 15 (29 de julio de 2022): 7659. http://dx.doi.org/10.3390/app12157659.
Texto completoZhang, Minxue, Ning Xu y Xin Geng. "Feature-Induced Label Distribution for Learning with Noisy Labels". Pattern Recognition Letters 155 (marzo de 2022): 107–13. http://dx.doi.org/10.1016/j.patrec.2022.02.011.
Texto completoLi, Guozheng, Peng Wang, Qiqing Luo, Yanhe Liu y Wenjun Ke. "Online Noisy Continual Relation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 11 (26 de junio de 2023): 13059–66. http://dx.doi.org/10.1609/aaai.v37i11.26534.
Texto completoYan, Shaotian, Xiang Tian, Rongxin Jiang y Yaowu Chen. "FGCM: Noisy Label Learning via Fine-Grained Confidence Modeling". Applied Sciences 12, n.º 22 (10 de noviembre de 2022): 11406. http://dx.doi.org/10.3390/app122211406.
Texto completoWang, Zixiao, Junwu Weng, Chun Yuan y Jue Wang. "Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junio de 2023): 2751–58. http://dx.doi.org/10.1609/aaai.v37i3.25375.
Texto completoWang, Deng-Bao, Yong Wen, Lujia Pan y Min-Ling Zhang. "Learning from Noisy Labels with Complementary Loss Functions". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 10111–19. http://dx.doi.org/10.1609/aaai.v35i11.17213.
Texto completoLiu, Xiaoli, Baoping Tang, Qikang Li y Qichao Yang. "Twin prototype networks with noisy label self-correction for fault diagnosis of wind turbine gearboxes". Measurement Science and Technology 34, n.º 3 (1 de diciembre de 2022): 035006. http://dx.doi.org/10.1088/1361-6501/aca3c3.
Texto completoDuan, Yunyan y Ou Wu. "Learning With Auxiliary Less-Noisy Labels". IEEE Transactions on Neural Networks and Learning Systems 28, n.º 7 (julio de 2017): 1716–21. http://dx.doi.org/10.1109/tnnls.2016.2546956.
Texto completoHan, Bo, Ivor W. Tsang, Ling Chen, Celina P. Yu y Sai-Fu Fung. "Progressive Stochastic Learning for Noisy Labels". IEEE Transactions on Neural Networks and Learning Systems 29, n.º 10 (octubre de 2018): 5136–48. http://dx.doi.org/10.1109/tnnls.2018.2792062.
Texto completoZhao, Pan, Long Tang y Zhigeng Pan. "Zero-Shot Learning with Noisy Labels". Procedia Computer Science 221 (2023): 763–72. http://dx.doi.org/10.1016/j.procs.2023.08.049.
Texto completoXu, Ran, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang y Carl Yang. "Neighborhood-Regularized Self-Training for Learning with Few Labels". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10611–19. http://dx.doi.org/10.1609/aaai.v37i9.26260.
Texto completoJian, Ling, Fuhao Gao, Peng Ren, Yunquan Song y Shihua Luo. "A Noise-Resilient Online Learning Algorithm for Scene Classification". Remote Sensing 10, n.º 11 (20 de noviembre de 2018): 1836. http://dx.doi.org/10.3390/rs10111836.
Texto completoZhang, Youqiang, Jin Sun, Hao Shi, Zixian Ge, Qiqiong Yu, Guo Cao y Xuesong Li. "Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels". Remote Sensing 15, n.º 10 (12 de mayo de 2023): 2543. http://dx.doi.org/10.3390/rs15102543.
Texto completoZhang, Yaojie, Huahu Xu, Junsheng Xiao y Minjie Bian. "JoSDW: Combating Noisy Labels by Dynamic Weight". Future Internet 14, n.º 2 (2 de febrero de 2022): 50. http://dx.doi.org/10.3390/fi14020050.
Texto completoChen, Mingxia, Jing Wang, Xueqing Li y Xiaolong Sun. "Robust Semi-Supervised Manifold Learning Algorithm for Classification". Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/2382803.
Texto completoLi, Weiwei, Yuqing Lu, Lei Chen y Xiuyi Jia. "Label distribution learning with noisy labels via three-way decisions". International Journal of Approximate Reasoning 150 (noviembre de 2022): 19–34. http://dx.doi.org/10.1016/j.ijar.2022.08.009.
Texto completoLong, Lingli, Yongjin Zhu, Jun Shao, Zheng Kong, Jian Li, Yanzheng Xiang y Xu Zhang. "NL2SQL Generation with Noise Labels based on Multi-task Learning". Journal of Physics: Conference Series 2294, n.º 1 (1 de junio de 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2294/1/012016.
Texto completoYan, Yan y Yuhong Guo. "Partial Label Learning with Batch Label Correction". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6575–82. http://dx.doi.org/10.1609/aaai.v34i04.6132.
Texto completoKong, Kyeongbo, Junggi Lee, Youngchul Kwak, Young-Rae Cho, Seong-Eun Kim y Woo-Jin Song. "Penalty based robust learning with noisy labels". Neurocomputing 489 (junio de 2022): 112–27. http://dx.doi.org/10.1016/j.neucom.2022.02.030.
Texto completoSun, Yi, Yan Tian, Yiping Xu y Jianxiang Li. "Limited Gradient Descent: Learning With Noisy Labels". IEEE Access 7 (2019): 168296–306. http://dx.doi.org/10.1109/access.2019.2954547.
Texto completoSun, Haoliang, Chenhui Guo, Qi Wei, Zhongyi Han y Yilong Yin. "Learning to rectify for robust learning with noisy labels". Pattern Recognition 124 (abril de 2022): 108467. http://dx.doi.org/10.1016/j.patcog.2021.108467.
Texto completoLin, Chuang, Shanxin Guo, Jinsong Chen, Luyi Sun, Xiaorou Zheng, Yan Yang y Yingfei Xiong. "Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification". Remote Sensing 13, n.º 9 (27 de abril de 2021): 1689. http://dx.doi.org/10.3390/rs13091689.
Texto completoZhao, QiHao, Wei Hu, Yangyu Huang y Fan Zhang. "P-DIFF+: Improving learning classifier with noisy labels by Noisy Negative Learning loss". Neural Networks 144 (diciembre de 2021): 1–10. http://dx.doi.org/10.1016/j.neunet.2021.07.024.
Texto completoXu, Ning, Yun-Peng Liu y Xin Geng. "Partial Multi-Label Learning with Label Distribution". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6510–17. http://dx.doi.org/10.1609/aaai.v34i04.6124.
Texto completoYao, Jiangchao, Hao Wu, Ya Zhang, Ivor W. Tsang y Jun Sun. "Safeguarded Dynamic Label Regression for Noisy Supervision". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9103–10. http://dx.doi.org/10.1609/aaai.v33i01.33019103.
Texto completoSun, Lijuan, Ping Ye, Gengyu Lyu, Songhe Feng, Guojun Dai y Hua Zhang. "Weakly-supervised multi-label learning with noisy features and incomplete labels". Neurocomputing 413 (noviembre de 2020): 61–71. http://dx.doi.org/10.1016/j.neucom.2020.06.101.
Texto completoLiu, Kun-Lin, Wu-Jun Li y Minyi Guo. "Emoticon Smoothed Language Models for Twitter Sentiment Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 26, n.º 1 (20 de septiembre de 2021): 1678–84. http://dx.doi.org/10.1609/aaai.v26i1.8353.
Texto completoBüttner, Martha, Lisa Schneider, Aleksander Krasowski, Joachim Krois, Ben Feldberg y Falk Schwendicke. "Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs". Journal of Clinical Medicine 12, n.º 9 (23 de abril de 2023): 3058. http://dx.doi.org/10.3390/jcm12093058.
Texto completoZhang, Qian, Feifei Lee, Ya-gang Wang, Ran Miao, Lei Chen y Qiu Chen. "An improved noise loss correction algorithm for learning from noisy labels". Journal of Visual Communication and Image Representation 72 (octubre de 2020): 102930. http://dx.doi.org/10.1016/j.jvcir.2020.102930.
Texto completoZhao, Yue, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann y Ahmed Awadallah. "ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junio de 2023): 4937–45. http://dx.doi.org/10.1609/aaai.v37i4.25620.
Texto completoLuo, Yaoru, Guole Liu, Yuanhao Guo y Ge Yang. "Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junio de 2022): 1908–16. http://dx.doi.org/10.1609/aaai.v36i2.20085.
Texto completoZheng, Kecheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang y Zheng-Jun Zha. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 4 (18 de mayo de 2021): 3538–46. http://dx.doi.org/10.1609/aaai.v35i4.16468.
Texto completoWang, Ziyang y Irina Voiculescu. "Dealing with Unreliable Annotations: A Noise-Robust Network for Semantic Segmentation through A Transformer-Improved Encoder and Convolution Decoder". Applied Sciences 13, n.º 13 (7 de julio de 2023): 7966. http://dx.doi.org/10.3390/app13137966.
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