Готові списки джерел за темами / Interpolation-Based data augmentation

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Добірка наукової літератури з теми "Interpolation-Based data augmentation"

Автор: Grafiati

Опубліковано: 28 вересня 2024

Оновлено: 31 липня 2025

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Статті в журналах з теми "Interpolation-Based data augmentation"

Oh, Cheolhwan, Seungmin Han, and Jongpil Jeong. "Time-Series Data Augmentation based on Interpolation." Procedia Computer Science 175 (2020): 64–71. http://dx.doi.org/10.1016/j.procs.2020.07.012.

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Li, Yuliang, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. "Data augmentation for ML-driven data preparation and integration." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3182–85. http://dx.doi.org/10.14778/3476311.3476403.

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Анотація:

In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning.

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Huang, Chenhui, and Akinobu Shibuya. "High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data." Remote Sensing 12, no. 12 (2020): 1991. http://dx.doi.org/10.3390/rs12121991.

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Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to me

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Tsourtis, Anastasios, Georgios Papoutsoglou, and Yannis Pantazis. "GAN-Based Training of Semi-Interpretable Generators for Biological Data Interpolation and Augmentation." Applied Sciences 12, no. 11 (2022): 5434. http://dx.doi.org/10.3390/app12115434.

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Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of collected data, the under-representation of important cell (sub-)populations negatively affects down-stream analysis and its robustness. Therefore, the enrichment of biological datasets with samples that belong to a rare state or manifold is overall advantageous. In this work, we train families of generative models via the minimization of Rényi diver

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Becerra-Suarez, Fray L., Halyn Alvarez-Vasquez, and Manuel G. Forero. "Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise." Technologies 13, no. 4 (2025): 141. https://doi.org/10.3390/technologies13040141.

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Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent in interpolation-based techniques. Five classifiers, including XGBoost and a convolutional neural network (CNN), were evaluated on augmented datasets. XGBoost achieved superior performance with Gaussian noi

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Li, Jinyuan, Wenqing Wan, Yong Feng, and Jinglong Chen. "Meta-task interpolation-based data augmentation for imbalanced health status recognition of complex equipment." Computers in Industry 165 (February 2025): 104226. https://doi.org/10.1016/j.compind.2024.104226.

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Bi, Xiao-ying, Bo Li, Wen-long Lu, and Xin-zhi Zhou. "Daily runoff forecasting based on data-augmented neural network model." Journal of Hydroinformatics 22, no. 4 (2020): 900–915. http://dx.doi.org/10.2166/hydro.2020.017.

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Abstract Accurate daily runoff prediction plays an important role in the management and utilization of water resources. In order to improve the accuracy of prediction, this paper proposes a deep neural network (CAGANet) composed of a convolutional layer, an attention mechanism, a gated recurrent unit (GRU) neural network, and an autoregressive (AR) model. Given that the daily runoff sequence is abrupt and unstable, it is difficult for a single model and combined model to obtain high-precision daily runoff predictions directly. Therefore, this paper uses a linear interpolation method to enhance

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Halevy, Karina, Karly Hou, and Charumathi Badrinath. "Who’s the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17014–22. https://doi.org/10.1609/aaai.v39i16.33870.

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Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on metrics that do not capture model uncertainty and on datasets with only one, relatively large, minority group. As a remedy, multicalibration has been introduced to measure fairness while accommodating uncertainty and accounting for multiple minority groups. However, existing methods of improving multicalibration involve reducing initial training data to create a holdout set for post-processing, which is not ideal wh

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de Rojas, Ana Lazcano. "Data augmentation in economic time series: Behavior and improvements in predictions." AIMS Mathematics 8, no. 10 (2023): 24528–44. http://dx.doi.org/10.3934/math.20231251.

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<abstract> <p>The performance of neural networks and statistical models in time series prediction is conditioned by the amount of data available. The lack of observations is one of the main factors influencing the representativeness of the underlying patterns and trends. Using data augmentation techniques based on classical statistical techniques and neural networks, it is possible to generate additional observations and improve the accuracy of the predictions. The particular characteristics of economic time series make it necessary that data augmentation techniques do not signific

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Becerra-Suarez, Fray L., Luciani J. Jiménez-Fernández, Estrella D. Ticona-Tapia, José Rolando Cárdenas-Gonzáles, and Pepe Humberto Bustamante-Quintana. "SynKGen: A kernel PCA-Based oversampling method for enhanced credit card fraud detection." Revista Científica de Sistemas e Informática 5, no. 2 (2025): e952. https://doi.org/10.51252/rcsi.v5i2.952.

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Credit card fraud detection is a growing challenge in the financial domain due to data imbalance, where fraudulent transactions are minimal compared to legitimate ones. This study presents SynKGen, a data augmentation method using Kernel PCA with Gaussian perturbations to generate synthetic samples of the minority class, contrasting it with ADASYN and SMOTE. By introducing variance analysis with controlled perturbations in the minority class, the proposed approach mitigates the risks of overfitting associated with traditional interpolation-based techniques. Four classifiers, XGBoost, RandomFor

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Більше джерел

Дисертації з теми "Interpolation-Based data augmentation"

Venkataramanan, Shashanka. "Metric learning for instance and category-level visual representation." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS022.

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Le principal objectif de la vision par ordinateur est de permettre aux machines d'extraire des informations significatives à partir de données visuelles, telles que des images et des vidéos, et de tirer parti de ces informations pour effectuer une large gamme de tâches. À cette fin, de nombreuses recherches se sont concentrées sur le développement de modèles d'apprentissage profond capables de coder des représentations visuelles complètes et robustes. Une stratégie importante dans ce contexte consiste à préentraîner des modèles sur des ensembles de données à grande échelle, tels qu'ImageNet, p

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Частини книг з теми "Interpolation-Based data augmentation"

Wang, Yongjun, Fuyong Xu, Bin Wang, and Peiyu Liu. "Leveled Learning: An Interpolation-Based Data Augmentation Method on Few-Shot Text Classification." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7008-6_13.

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Rabah, Mohamed Louay, Nedra Mellouli, and Imed Riadh Farah. "Interpolation and Prediction of Piezometric Multivariate Time Series Based on Data Augmentation and Transformers." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47724-9_22.

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Tereikovska, Liudmyla, and Ihor Tereikovskyi. "MATHEMATICAL SUPPORT OF GEOMETRIC TRANSFORMATIONS OF IMAGES DURING DATA AUGMENTATION OF NEURON NETWORK TOOLS." In Science, technology and innovation in the context of global transformation. Publishing House “Baltija Publishing”, 2024. https://doi.org/10.30525/978-9934-26-499-3-12.

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Анотація:

One of the key problems in the field of increasing the efficiency of neural network tools intended for the analysis of graphic materials is the formation of representative training databases. A promising way to overcome this problem is to increase the size of the training sample by applying the augmentation of training examples due to geometric transformations. However, the modern mathematical apparatus for modifying the geometric parameters of images has shortcomings that can reduce the quality of the obtained images or lead to their insufficient compliance with the tasks. The purpose of the

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Song, Xingyu, Zhan Li, Shi Chen, Xin-Qiang Cai, and Kazuyuki Demachi. "An Animation-Based Augmentation Approach for Action Recognition from Discontinuous Video." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240478.

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Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Networks (CNNs), these models suffer performance declines when trained with discontinuous video frames, which is a frequent scenario in real-world settings. This decline primarily results from the loss of temporal continuity, which is crucial for understanding the semantics of human actions. To overcome this issue, we introduce the 4A (Action Animation-based Augmentation Approach) pipeline, which employs a series of sophi

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Тези доповідей конференцій з теми "Interpolation-Based data augmentation"

Ye, Mao, Haitao Wang, and Zheqian Chen. "MSMix: An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup." In 4th International Conference on Natural Language Processing and Machine Learning. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130806.

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Анотація:

To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup). This method feeds two different samples to the same deep neural network model, and then randomly select a specific layer and partially replace hidden features at that layer of one of the samples by the counterpart of the other. The mixed hidden features are fed to the model and go through the rest of the network. Two different selection strategies are also proposed to obtain richer hidden r

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Heo, Jaeseung, Seungbeom Lee, Sungsoo Ahn, and Dongwoo Kim. "EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/455.

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Анотація:

Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process between two graphs via edit operations. Moreover, our method introduces a context

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Li, Chen, Xutan Peng, Hao Peng, Jianxin Li, and Lihong Wang. "TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification via Structure-Sensitive Interpolation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/369.

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Анотація:

Compared with traditional sequential learning models, graph-based neural networks exhibit excellent properties when encoding text, such as the capacity of capturing global and local information simultaneously. Especially in the semi-supervised scenario, propagating information along the edge can effectively alleviate the sparsity of labeled data. In this paper, beyond the existing architecture of heterogeneous word-document graphs, for the first time, we investigate how to construct lightweight non-heterogeneous graphs based on different linguistic information to better serve free text represe

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