Artykuły w czasopismach na temat „Dataset noise”
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Jia, Qingrui, Xuhong Li, Lei Yu, Jiang Bian, Penghao Zhao, Shupeng Li, Haoyi Xiong i Dejing Dou. "Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 8041–49. http://dx.doi.org/10.1609/aaai.v37i7.25972.
Pełny tekst źródłaJiang, Gaoxia, Jia Zhang, Xuefei Bai, Wenjian Wang i Deyu Meng. "Which Is More Effective in Label Noise Cleaning, Correction or Filtering?" Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 11 (24.03.2024): 12866–73. http://dx.doi.org/10.1609/aaai.v38i11.29183.
Pełny tekst źródłaFu, Bo, Xiangyi Zhang, Liyan Wang, Yonggong Ren i Dang N. H. Thanh. "A blind medical image denoising method with noise generation network". Journal of X-Ray Science and Technology 30, nr 3 (15.04.2022): 531–47. http://dx.doi.org/10.3233/xst-211098.
Pełny tekst źródłaChoi, Hwiyong, Haesang Yang, Seungjun Lee i Woojae Seong. "Classification of Inter-Floor Noise Type/Position Via Convolutional Neural Network-Based Supervised Learning". Applied Sciences 9, nr 18 (7.09.2019): 3735. http://dx.doi.org/10.3390/app9183735.
Pełny tekst źródłaHossain, Sadat, i Bumshik Lee. "NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise". Sensors 23, nr 1 (26.12.2022): 251. http://dx.doi.org/10.3390/s23010251.
Pełny tekst źródłaZhang, Rui, Zhenghao Chen, Sanxing Zhang, Fei Song, Gang Zhang, Quancheng Zhou i Tao Lei. "Remote Sensing Image Scene Classification with Noisy Label Distillation". Remote Sensing 12, nr 15 (24.07.2020): 2376. http://dx.doi.org/10.3390/rs12152376.
Pełny tekst źródłaVan Hulse, Jason, Taghi M. Khoshgoftaar i Amri Napolitano. "Evaluating the Impact of Data Quality on Sampling". Journal of Information & Knowledge Management 10, nr 03 (wrzesień 2011): 225–45. http://dx.doi.org/10.1142/s021964921100295x.
Pełny tekst źródłaNogales, Alberto, Javier Caracuel-Cayuela i Álvaro J. García-Tejedor. "Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression". Applied Sciences 14, nr 2 (15.01.2024): 740. http://dx.doi.org/10.3390/app14020740.
Pełny tekst źródłaKramberger, Tin, i Božidar Potočnik. "LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training". Applied Sciences 10, nr 14 (17.07.2020): 4913. http://dx.doi.org/10.3390/app10144913.
Pełny tekst źródłaShi, Haoxiang, Jun Ai, Jingyu Liu i Jiaxi Xu. "Improving Software Defect Prediction in Noisy Imbalanced Datasets". Applied Sciences 13, nr 18 (19.09.2023): 10466. http://dx.doi.org/10.3390/app131810466.
Pełny tekst źródłaSingha, Samir, i Syed Hassan. "ENHANCING THE CLASSIFICATION ACCURACY OF NOISY DATASET BY FUSING CORRELATION BASED FEATURE SELECTION WITH K-NEAREST NEIGHBOUR". Oriental journal of computer science and technology 10, nr 2 (15.05.2017): 282–90. http://dx.doi.org/10.13005/ojcst/10.02.05.
Pełny tekst źródłaFOLLECO, ANDRES, i TAGHI KHOSHGOFTAAR. "ATTRIBUTE NOISE DETECTION USING MULTI-RESOLUTION ANALYSIS". International Journal of Reliability, Quality and Safety Engineering 13, nr 03 (czerwiec 2006): 267–88. http://dx.doi.org/10.1142/s0218539306002252.
Pełny tekst źródłaLi, Qiang, Ziqi Xie i Lihong Wang. "Robust Subspace Clustering with Block Diagonal Representation for Noisy Image Datasets". Electronics 12, nr 5 (5.03.2023): 1249. http://dx.doi.org/10.3390/electronics12051249.
Pełny tekst źródłaIhler, Sontje, i Felix Kuhnke. "AUC margin loss for limited, imbalanced and noisy medical image diagnosis – a case study on CheXpert5000". Current Directions in Biomedical Engineering 9, nr 1 (1.09.2023): 658–61. http://dx.doi.org/10.1515/cdbme-2023-1165.
Pełny tekst źródłaGarcía-Mendoza, Juan-Luis, Luis Villaseñor-Pineda, Felipe Orihuela-Espina i Lázaro Bustio-Martínez. "An autoencoder-based representation for noise reduction in distant supervision of relation extraction". Journal of Intelligent & Fuzzy Systems 42, nr 5 (31.03.2022): 4523–29. http://dx.doi.org/10.3233/jifs-219241.
Pełny tekst źródłaAL-Akhras, Mousa, Abdulmajeed Alshunaybir, Hani Omar i Samah Alhazmi. "Botnet attacks detection in IoT environment using machine learning techniques". International Journal of Data and Network Science 7, nr 4 (2023): 1683–706. http://dx.doi.org/10.5267/j.ijdns.2023.7.021.
Pełny tekst źródłaLee, Yongju, Sungjun Jang, Han Byeol Bae, Taejae Jeon i Sangyoun Lee. "Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation". Sensors 24, nr 10 (18.05.2024): 3212. http://dx.doi.org/10.3390/s24103212.
Pełny tekst źródłaSantiago-Chaparro, Kelvin R., i David A. Noyce. "Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures". Transportation Research Record: Journal of the Transportation Research Board 2673, nr 11 (10.06.2019): 150–60. http://dx.doi.org/10.1177/0361198119852607.
Pełny tekst źródłaMurakami, Reina, Valentin Grave, Osamu Fukuda, Hiroshi Okumura i Nobuhiko Yamaguchi. "Improved Training of CAE-Based Defect Detectors Using Structural Noise". Applied Sciences 11, nr 24 (17.12.2021): 12062. http://dx.doi.org/10.3390/app112412062.
Pełny tekst źródłaNorthcutt, Curtis, Lu Jiang i Isaac Chuang. "Confident Learning: Estimating Uncertainty in Dataset Labels". Journal of Artificial Intelligence Research 70 (14.04.2021): 1373–411. http://dx.doi.org/10.1613/jair.1.12125.
Pełny tekst źródłaWang, Zi-yang, Xiao-yi Luo i Jun Liang. "A Label Noise Robust Stacked Auto-Encoder Algorithm for Inaccurate Supervised Classification Problems". Mathematical Problems in Engineering 2019 (14.05.2019): 1–19. http://dx.doi.org/10.1155/2019/2182616.
Pełny tekst źródłaBhatia, Anshul, Anuradha Chug, Amit Prakash Singh i Dinesh Singh. "A hybrid approach for noise reduction-based optimal classifier using genetic algorithm: A case study in plant disease prediction". Intelligent Data Analysis 26, nr 4 (11.07.2022): 1023–49. http://dx.doi.org/10.3233/ida-216011.
Pełny tekst źródłaBilla, Wagner S., Rogério G. Negri i Leonardo B. L. Santos. "WB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets". Eng 4, nr 4 (25.09.2023): 2497–513. http://dx.doi.org/10.3390/eng4040142.
Pełny tekst źródłaSagarika, Namasani, Bommadi Sreenija Reddy, Vanka Varshitha, Kodavati Geetanjali, N. V. Ganapathi Raju i Latha Kunaparaju. "Sarcasm Discernment on Social Media Platform". E3S Web of Conferences 309 (2021): 01037. http://dx.doi.org/10.1051/e3sconf/202130901037.
Pełny tekst źródłaGuan, Qingji, Qinrun Chen i Yaping Huang. "An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels". Algorithms 16, nr 5 (4.05.2023): 239. http://dx.doi.org/10.3390/a16050239.
Pełny tekst źródłaZhao, Na, i Gim Hee Lee. "Robust Visual Recognition with Class-Imbalanced Open-World Noisy Data". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 15 (24.03.2024): 16989–97. http://dx.doi.org/10.1609/aaai.v38i15.29642.
Pełny tekst źródłaXi, Mengfei, Jie Li, Zhilin He, Minmin Yu i Fen Qin. "NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images". Remote Sensing 15, nr 1 (25.12.2022): 108. http://dx.doi.org/10.3390/rs15010108.
Pełny tekst źródłaOyewola, David Opeoluwa, Emmanuel Gbenga Dada, Sanjay Misra i Robertas Damaševičius. "Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques". Information 12, nr 12 (19.12.2021): 528. http://dx.doi.org/10.3390/info12120528.
Pełny tekst źródłaRasheed, Jawad, Ahmad B. Wardak, Adnan M. Abu-Mahfouz, Tariq Umer, Mirsat Yesiltepe i Sadaf Waziry. "An Efficient Machine Learning-Based Model to Effectively Classify the Type of Noises in QR Code: A Hybrid Approach". Symmetry 14, nr 10 (8.10.2022): 2098. http://dx.doi.org/10.3390/sym14102098.
Pełny tekst źródłaWang, Zixiao, Junwu Weng, Chun Yuan i Jue Wang. "Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 3 (26.06.2023): 2751–58. http://dx.doi.org/10.1609/aaai.v37i3.25375.
Pełny tekst źródłaMoura, Kecia G., Ricardo B. C. Prudêncio i George D. C. Cavalcanti. "Label noise detection under the noise at random model with ensemble filters". Intelligent Data Analysis 26, nr 5 (5.09.2022): 1119–38. http://dx.doi.org/10.3233/ida-215980.
Pełny tekst źródłaSingh, Abhishek, i Anil Kumar. "Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery". Journal of Modeling and Optimization 11, nr 1 (15.06.2019): 51–56. http://dx.doi.org/10.32732/jmo.2019.11.1.51.
Pełny tekst źródłaAkyel, Cihan, i Nursal Arıcı. "LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer". Mathematics 10, nr 5 (25.02.2022): 736. http://dx.doi.org/10.3390/math10050736.
Pełny tekst źródłaYi, Qian, Guixuan Zhang i Shuwu Zhang. "Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction". Computational Intelligence and Neuroscience 2021 (1.11.2021): 1–10. http://dx.doi.org/10.1155/2021/6110885.
Pełny tekst źródłaYoo, Seok Bong, i Mikyong Han. "SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises". Sensors 19, nr 8 (25.04.2019): 1939. http://dx.doi.org/10.3390/s19081939.
Pełny tekst źródłaGuan, Donghai, Maqbool Hussain, Weiwei Yuan, Asad Masood Khattak, Muhammad Fahim i Wajahat Ali Khan. "Enhanced Label Noise Filtering with Multiple Voting". Applied Sciences 9, nr 23 (21.11.2019): 5031. http://dx.doi.org/10.3390/app9235031.
Pełny tekst źródłaDelisle, J. B., N. Hara i D. Ségransan. "Efficient modeling of correlated noise". Astronomy & Astrophysics 638 (czerwiec 2020): A95. http://dx.doi.org/10.1051/0004-6361/201936906.
Pełny tekst źródłaGarg, Siddhant, Thuy Vu i Alessandro Moschitti. "TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 7780–88. http://dx.doi.org/10.1609/aaai.v34i05.6282.
Pełny tekst źródłaCheng, Hu, Sophia Vinci-Booher, Jian Wang, Bradley Caron, Qiuting Wen, Sharlene Newman i Franco Pestilli. "Denoising diffusion weighted imaging data using convolutional neural networks". PLOS ONE 17, nr 9 (15.09.2022): e0274396. http://dx.doi.org/10.1371/journal.pone.0274396.
Pełny tekst źródłaXiong, Shuguang, Huitao Zhang i Meng Wang. "Ensemble Model of Attention Mechanism-Based DCGAN and Autoencoder for Noised OCR Classification". Journal of Electronic & Information Systems 4, nr 1 (31.03.2022): 33–41. http://dx.doi.org/10.30564/jeis.v4i1.6725.
Pełny tekst źródłaSineglazov, Victor, i Kyrylo Lesohorskyi. "On Noise Effect in Semi-supervised Learning". Electronics and Control Systems 1, nr 71 (27.06.2022): 9–15. http://dx.doi.org/10.18372/1990-5548.71.16816.
Pełny tekst źródłaCao, Like, Jie Ling i Xiaohui Xiao. "Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet". Sensors 20, nr 17 (31.08.2020): 4922. http://dx.doi.org/10.3390/s20174922.
Pełny tekst źródłaHsieh, Ming-En, i Vincent Tseng. "Boosting Multi-task Learning Through Combination of Task Labels - with Applications in ECG Phenotyping". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 7771–79. http://dx.doi.org/10.1609/aaai.v35i9.16949.
Pełny tekst źródłaLi, Gang, Jan Zrimec, Boyang Ji, Jun Geng, Johan Larsbrink, Aleksej Zelezniak, Jens Nielsen i Martin KM Engqvist. "Performance of Regression Models as a Function of Experiment Noise". Bioinformatics and Biology Insights 15 (styczeń 2021): 117793222110203. http://dx.doi.org/10.1177/11779322211020315.
Pełny tekst źródłaLiu, Haiqing, Daoxing Li i Yuancheng Li. "Confident sequence learning: A sequence class-label noise filtering technique to improve scene digit recognition". Journal of Intelligent & Fuzzy Systems 40, nr 5 (22.04.2021): 9345–59. http://dx.doi.org/10.3233/jifs-201825.
Pełny tekst źródłaOguntunde, Pelumi E., Hilary I. Okagbue, Omoleye A. Oguntunde i Oluwole A. Odetunmibi. "A Study of Noise Pollution Measurements and Possible Effects on Public Health in Ota Metropolis, Nigeria". Open Access Macedonian Journal of Medical Sciences 7, nr 8 (29.04.2019): 1391–95. http://dx.doi.org/10.3889/oamjms.2019.234.
Pełny tekst źródłaZiyadinov, Vadim, i Maxim Tereshonok. "Noise Immunity and Robustness Study of Image Recognition Using a Convolutional Neural Network". Sensors 22, nr 3 (6.02.2022): 1241. http://dx.doi.org/10.3390/s22031241.
Pełny tekst źródłaZhou, Ping, Jin Lei Wang, Xian Kai Chen i Guan Jun Zhang. "Membership Calculation Based on Dimension Hierarchical Division". Applied Mechanics and Materials 475-476 (grudzień 2013): 312–17. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.312.
Pełny tekst źródłaChauhan, Neha, Tsuyoshi Isshiki i Dongju Li. "Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies". Acoustics 6, nr 2 (14.05.2024): 439–69. http://dx.doi.org/10.3390/acoustics6020024.
Pełny tekst źródłaYoudale, Chris, Simon Shilton i James Trow. "Impact of Ground Cover Dataset Selection on CNOSSOS-EU Calculated Levels". INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, nr 3 (1.02.2023): 4674–81. http://dx.doi.org/10.3397/in_2022_0676.
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