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Data Augmentation for Hypernymy Detection [PDF]
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021 The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data.
Thomas Kober 0001 +3 more
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An adaptive fusion-based data augmentation method for abstract dialogue summarization [PDF]
PeerJ Computer ScienceThe dialogue summarization is necessary for information retrieval, and the training of abstract dialogue summarization models heavily rely on large amounts of labeled data.
Weihao Li +4 more
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Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation [PDF]
PeerJ Computer ScienceData augmentation is a critical technique for enhancing model performance in scenarios with limited, sparse, or imbalanced datasets. While existing methods often focus on homogeneous data types (e.g., continuous-only or categorical-only), real-world ...
Mohsen Ben Hassine, Lamine Mili
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Review of Image Data Augmentation in Computer Vision
Jisuanji kexue yu tansuo, 2021 Deep learning is a promising solution for computer vision at present. To solve the computer vision problem, it requires massive and high-quality image training datasets.
LIN Chengchuang, SHAN Chun, ZHAO Gansen, YANG Zhirong, PENG Jing, CHEN Shaojie, HUANG Runhua, LI Zhuangwei, YI Xusheng, DU Jiahua, LI Shuangyin, LUO Haoyu, FAN Xiaomao, CHEN Bingchuan
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Data augmentation for models based on rejection sampling [PDF]
, 2015 We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm.
Dunson, David, Lin, Lizhen, Rao, Vinayak
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Transfer Incremental Learning Using Data Augmentation
Applied Sciences, 2018 Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which ...
Ghouthi Boukli Hacene +4 more
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Text Data Augmentation for the Korean Language
Applied Sciences, 2022 Data augmentation (DA) is a universal technique to reduce overfitting and improve the robustness of machine learning models by increasing the quantity and variety of the training dataset.
Dang Thanh Vu +3 more
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Dynamic causal model application on hierarchical human motor control estimation in visuomotor tasks
Frontiers in NeurologyIntroductionIn brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them.
Ningjia Yang +6 more
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Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
BioData Mining, 2021 Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values.
Jacqueline Beinecke, Dominik Heider
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Implicit Semantic Data Augmentation for Hand Pose Estimation
IEEE Access, 2022 Data augmentation is a well-known technique used for improving the generalization performance of modern neural networks. After the success of several traditional random data augmentation for images (including flipping, translation, or rotation), a recent
Kyeongeun Seo +3 more
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