Academic literature on the topic 'Dataset noise'
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Journal articles on the topic "Dataset noise":
Jia, Qingrui, Xuhong Li, Lei Yu, Jiang Bian, Penghao Zhao, Shupeng Li, Haoyi Xiong, and Dejing Dou. "Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8041–49. http://dx.doi.org/10.1609/aaai.v37i7.25972.
Jiang, Gaoxia, Jia Zhang, Xuefei Bai, Wenjian Wang, and Deyu Meng. "Which Is More Effective in Label Noise Cleaning, Correction or Filtering?" Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12866–73. http://dx.doi.org/10.1609/aaai.v38i11.29183.
Fu, Bo, Xiangyi Zhang, Liyan Wang, Yonggong Ren, and Dang N. H. Thanh. "A blind medical image denoising method with noise generation network." Journal of X-Ray Science and Technology 30, no. 3 (April 15, 2022): 531–47. http://dx.doi.org/10.3233/xst-211098.
Choi, Hwiyong, Haesang Yang, Seungjun Lee, and Woojae Seong. "Classification of Inter-Floor Noise Type/Position Via Convolutional Neural Network-Based Supervised Learning." Applied Sciences 9, no. 18 (September 7, 2019): 3735. http://dx.doi.org/10.3390/app9183735.
Hossain, Sadat, and Bumshik Lee. "NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise." Sensors 23, no. 1 (December 26, 2022): 251. http://dx.doi.org/10.3390/s23010251.
Zhang, Rui, Zhenghao Chen, Sanxing Zhang, Fei Song, Gang Zhang, Quancheng Zhou, and Tao Lei. "Remote Sensing Image Scene Classification with Noisy Label Distillation." Remote Sensing 12, no. 15 (July 24, 2020): 2376. http://dx.doi.org/10.3390/rs12152376.
Van Hulse, Jason, Taghi M. Khoshgoftaar, and Amri Napolitano. "Evaluating the Impact of Data Quality on Sampling." Journal of Information & Knowledge Management 10, no. 03 (September 2011): 225–45. http://dx.doi.org/10.1142/s021964921100295x.
Nogales, Alberto, Javier Caracuel-Cayuela, and Álvaro J. García-Tejedor. "Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression." Applied Sciences 14, no. 2 (January 15, 2024): 740. http://dx.doi.org/10.3390/app14020740.
Kramberger, Tin, and Božidar Potočnik. "LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training." Applied Sciences 10, no. 14 (July 17, 2020): 4913. http://dx.doi.org/10.3390/app10144913.
Shi, Haoxiang, Jun Ai, Jingyu Liu, and Jiaxi Xu. "Improving Software Defect Prediction in Noisy Imbalanced Datasets." Applied Sciences 13, no. 18 (September 19, 2023): 10466. http://dx.doi.org/10.3390/app131810466.
Dissertations / Theses on the topic "Dataset noise":
Kvist, Eric, and Rhodin Sandro Lockvall. "A comparative study between MLP and CNN for noise reduction on images : The impact of different input dataset sizes and the impact of different types of noise on performance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259654.
Bilder som är utsatta för brus är ett problem som kan adresseras genom att utföra brusreduktion med hjälp av neurala nätverk. I denna studie analyseras effekt-skillnader i brusredusering av bilder för två olika typer av neurala nätverk, en Multilayer Perceptron (MLP) och ett konvolutionellt neuralt nätverk (CNN). Fokus ligger specifikt på hur indatans storlek under träningen, är påverkad av två olika typer av neuronnätverk samt hur bra dessa två neurala nätverk presterar när de reducerar olika typer av brus. Detta i ett försök att avgöra om användningen av den modernare typen av nätverk, CNN har högre prestanda än den äldre typen, MLP för brusreducering. Resultaten visar som förväntat att MLP:n fungerar sämre än CNN:n, också att effekten av indatans storlek och valet av brus att reduceras är, trots att de båda har en stor inverkan på prestandan, inte lika viktigt som valet av neuralt nätverk.
Hrabina, Martin. "VÝVOJ ALGORITMŮ PRO ROZPOZNÁVÁNÍ VÝSTŘELŮ." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-409087.
Al, Jurdi Wissam. "Towards next generation recommender systems through generic data quality." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD005.
Recommender systems are essential for filtering online information and delivering personalized content, thereby reducing the effort users need to find relevant information. They can be content-based, collaborative, or hybrid, each with a unique recommendation approach. These systems are crucial in various fields, including e-commerce, where they help customers find pertinent products, enhancing user experience and increasing sales. A significant aspect of these systems is the concept of unexpectedness, which involves discovering new and surprising items. This feature, while improving user engagement and experience, is complex and subjective, requiring a deep understanding of serendipitous recommendations for its measurement and optimization. Natural noise, an unpredictable data variation, can influence serendipity in recommender systems. It can introduce diversity and unexpectedness in recommendations, leading to pleasant surprises. However, it can also reduce recommendation relevance, causing user frustration. Therefore, it is crucial to design systems that balance natural noise and serendipity. Inconsistent user information due to natural noise can negatively impact recommender systems, leading to lower-quality recommendations. Current evaluation methods often overlook critical user-oriented factors, making noise detection a challenge. To provide powerful recommendations, it’s important to consider diverse user profiles, eliminate noise in datasets, and effectively present users with relevant content from vast data catalogs. This thesis emphasizes the role of serendipity in enhancing recommender systems and preventing filter bubbles. It proposes serendipity-aware techniques to manage noise, identifies algorithm flaws, suggests a user-centric evaluation method, and proposes a community-based architecture for improved performance. It highlights the need for a system that balances serendipity and considers natural noise and other performance factors. The objectives, experiments, and tests aim to refine recommender systems and offer a versatile assessment approach
Fonseca, Eduardo. "Training sound event classifiers using different types of supervision." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673067.
El 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.
CAPPOZZO, ANDREA. "Robust model-based classification and clustering: advances in learning from contaminated datasets." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262919.
At the time of writing, an ever-increasing amount of data is collected every day, with its volume estimated to be doubling every two years. Thanks to the technological advancements, datasets are becoming massive in terms of size and substantially more complex in nature. Nevertheless, this abundance of ``raw information'' does come at a price: wrong measurements, data-entry errors, breakdowns of automatic collection systems and several other causes may ultimately undermine the overall data quality. To this extent, robust methods have a central role in properly converting contaminated ``raw information'' to trustworthy knowledge: a primary goal of any statistical analysis. The present manuscript presents novel methodologies for performing reliable inference, within the model-based classification and clustering framework, in presence of contaminated data. First, we propose a robust modification to a family of semi-supervised patterned models, for accomplishing classification when dealing with both class and attribute noise. Second, we develop a discriminant analysis method for anomaly and novelty detection, with the final aim of discovering label noise, outliers and unobserved classes in an unlabelled dataset. Third, we introduce two robust variable selection methods, that effectively perform high-dimensional discrimination within an adulterated scenario.
Rehn, Ruben, and Ricky Molén. "The ghost in the machine : Exploring the impact of noise in datasets used for graph-based action recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302503.
Mänsklig rörelseigenkänning (en. human action recognition) är forskningsområdet ägnat åt att känna igen mänskliga rörelser från videodata. För att kunna jämföra olika algoritmer inom området förekommer ofta ett standardiserat datasetet, NTU-RGB+D, som bland annat innehåller skelettrepresentationer av människor som utför rörelser. Trots datasetets vida användning inom rörelseigenkänning innehåller det vad som i denna uppsats benämns spökkroppar (en. ghost bodies). Dessa artefakter i datasetet är skelettrepresentationer som felaktigt klassats som att de tillhör en människokropp när de i själva verket utgör något annat icke-mänskligt objekt i videodatan. Experimentet som redogörs för i denna uppsats har ägnats åt att undersöka hur dessa spökkroppar påverkar rörelseigenkänningsprecisionen (en. action recognition accuracy) hos ett nutida riktad-graf-baserat neuralt nätverk (en. directed graph neural network, DGNN). Resultaten visar att igenkänningsprecisionen tycks öka med 1,79 procentenheter när grafnätverket tränas utan förekomster av spökkroppar. Resultaten bör dock tolkas med försiktighet då den igenkänningsprecision som rapporterats för grafnätverket i originalexperimentet inte kunde replikeras. Trots detta utgör NTU ett så pass viktigt dataset för forskning inom rörelseigenkänning, att vidare analys och förbättring av datasetet med avseende på spökkropparna är att rekommendera. Även om resultaten inte kan generaliseras bortom det grafnätverk som experimentet utfördes med, pekar ändå den uppmätta skillnaden i igenkänningsprecision på vikten av vidare analys vad gäller spökkroppars inverkan på moderna algoritmer inom rörelseigenkänning.
Jia, Sen. "Data from the wild in computer vision : generating and exploiting large scale and noisy datasets." Thesis, University of Bristol, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738203.
Osman, Ahmad. "Automated evaluation of three dimensional ultrasonic datasets." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00995119.
Liu, Qian. "Deep spiking neural networks." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/deep-spiking-neural-networks(336e6a37-2a0b-41ff-9ffb-cca897220d6c).html.
Chen, Jun-An, and 陳俊安. "Using Fuzzy Support Vector Machine to Solve Imbalanced Datasets and Noise Problems." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/12559097922249906761.
朝陽科技大學
資訊工程系
103
This paper proposed a method that removes the redundant training data in order to retrieve the support vectors and introduces fuzzy support vector machine to solve imbalanced datasets problems. Firstly, all categories of training data were clustered and the probability of training data belongs to support vectors were computing, and then randomly remove the non-support vector so that the number of data in each category was reached balanced. Next, the degrees of membership of training data were calculated by using fuzzy k-nearest neighborhood algorithm, in order to identify and remove the noise. Finally, the data obtained from the above treatment are recombined to construct a fuzzy support vector machine. In this paper, UCI WCBD (Wisconsin Breast Cancer Dataset) repository was selected for the experiment. The experimental results that are achieved by the proposed method were compared to some well know techniques, i.e. the classical SMOTE approach, SBC approach, and SUNDO approach. Experimental results reveal that the proposed approach outperforms with other approaches.
Books on the topic "Dataset noise":
W, Spencer Roy, McNider Richard T, and United States. National Aeronautics and Space Administration., eds. Reducing noise in the MSU daily lower-tropospheric global temperature dataset. [Washington, D.C: National Aeronautics and Space Administration, 1995.
W, Spencer Roy, McNider Richard T, and United States. National Aeronautics and Space Administration., eds. Reducing noise in the MSU daily lower-tropospheric global temperature dataset. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Machine Learning Methods with Noisy, Incomplete or Small Datasets. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1288-4.
Book chapters on the topic "Dataset noise":
Alrashed, Tarfah, Dimitris Paparas, Omar Benjelloun, Ying Sheng, and Natasha Noy. "Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages." In The Semantic Web – ISWC 2021, 338–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88361-4_20.
Hagn, Korbinian, and Oliver Grau. "Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation." In Deep Neural Networks and Data for Automated Driving, 127–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_4.
Singstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology, 91–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.
Spreeuwers, Luuk, Maikel Schils, Raymond Veldhuis, and Una Kelly. "Practical Evaluation of Face Morphing Attack Detection Methods." In Handbook of Digital Face Manipulation and Detection, 351–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_16.
Frank, David, Keyan Fang, and Patrick Fonti. "Dendrochronology: Fundamentals and Innovations." In Stable Isotopes in Tree Rings, 21–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92698-4_2.
Mulder, Valentin, and Mathias Humbert. "Differential Privacy." In Trends in Data Protection and Encryption Technologies, 157–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33386-6_27.
Passonneau, Rebecca J., Cynthia Rudin, Axinia Radeva, and Zhi An Liu. "Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods." In Computational Linguistics and Intelligent Text Processing, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00382-0_7.
Stojanovski, David, Uxio Hermida, Pablo Lamata, Arian Beqiri, and Alberto Gomez. "Echo from Noise: Synthetic Ultrasound Image Generation Using Diffusion Models for Real Image Segmentation." In Simplifying Medical Ultrasound, 34–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44521-7_4.
Wang, Zehui, Luca Koroll, Wolfram Höpken, and Matthias Fuchs. "Analysis of Instagram Users’ Movement Pattern by Cluster Analysis and Association Rule Mining." In Information and Communication Technologies in Tourism 2022, 97–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_10.
Cai, Hua, Qing Xu, and Weilin Shen. "Complex Relative Position Encoding for Improving Joint Extraction of Entities and Relations." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 644–55. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_66.
Conference papers on the topic "Dataset noise":
Brummer, Benoit, and Christophe De Vleeschouwer. "Natural Image Noise Dataset." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2019. http://dx.doi.org/10.1109/cvprw.2019.00228.
Hu, Xiaomin, Ying Tang, Xinmu Zhu, Qi Qiu, Tao Zhu, and Chao Liu. "WNRoom Dataset: White Noise Dataset for Detecting the Status of Room." In 2023 IEEE 11th International Conference on Information, Communication and Networks (ICICN). IEEE, 2023. http://dx.doi.org/10.1109/icicn59530.2023.10392954.
Pio, Pedro B., Adriano Rivolli, André C. P. L. F. de Carvalho, and Luís P. F. Garcia. "Noise filter with hyperparameter recommendation: a meta-learning approach." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234295.
Xiang, Cheng, Li Ke, and Yan Jun. "Analyzing Dataset with Noise in Geometric Fashion." In Second International Conference on Information and Computing Science, ICIC 2009. IEEE, 2009. http://dx.doi.org/10.1109/icic.2009.137.
Price, G. "Susceptibility to intense impulse noise: evidence from the Albuquerque dataset." In 159th Meeting Acoustical Society of America/NOISE-CON 2010. ASA, 2010. http://dx.doi.org/10.1121/1.3478337.
Hsieh, Jiang. "Generation of training dataset for deep-learning noise reduction." In Physics of Medical Imaging, edited by Rebecca Fahrig, John M. Sabol, and Lifeng Yu. SPIE, 2023. http://dx.doi.org/10.1117/12.2647904.
Rücker, Susanna, and Alan Akbik. "CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset." In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.533.
Lew, C. L., C. MacBeth, and A. Elsheikh. "Deep Learning Application for Inverting Petrophysical Properties Directly from Seismic." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216433-ms.
Li, Ang, Qiuhong Ke, Xingjun Ma, Haiqin Weng, Zhiyuan Zong, Feng Xue, and Rui Zhang. "Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting." 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.2021/109.
Jayawardena, Lasal, and Prasan Yapa. "Parafusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity." In 5th International Conference on Artificial Intelligence and Big Data. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140418.
Reports on the topic "Dataset noise":
Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Eastern Canadian Arctic and the Hudson Bay Complex, Manitoba, Newfoundland and Labrador, Nunavut, Ontario, and Quebec. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330396.
Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Northern Appalachians Seismic Zone, New Brunswick and Nova Scotia. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329702.
Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Charlevoix Seismic Zone, southern Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332158.
Farahbod, A., and J. F. Cassidy. Spatial and temporal variations in seismic coda Q attenuation in the lower St. Lawrence region, southeastern Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332027.
Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.
Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Dia Internacional da Conscientização sobre o Ruído — INAD Brasil 2022. Sociedade Brasileira de Acústica (Sobrac), December 2022. http://dx.doi.org/10.55753/aev.v37e54.203.