14 papers · ranked by Valyu relevance
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force…
Brian Kenji Iwana, Seiichi Uchida, Friedhelm Schwenker
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem…
Khaled Alomar, Halil Ibrahim Aysel, Xiaohao Cai, Jérôme Gilles + 1 more
'Luminiţa Moraru'] In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for…
Jakub Nalepa, Michal Marcinkiewicz, Michal Kawulok
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and…
Nurdan Ayse Saran, Murat Saran, Fatih Nar, Sebastian Ventura
In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it…
Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini + 2 more
Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by…
Marwa Ibrahim, Mohammad Wedyan, Ryan Alturki, Muazzam A. Khan + 1 more
'Adel Al-Jumaily'] In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other…
Wei Wang, Zhaowei Shang, Chengxing Li
Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When…
Ji-Young Yoon, Gahgene Gweon, Yun Joo Yoo, Peida Zhan
Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can…
Dan Liu, Samer El Kababji, Nicholas Mitsakakis, Lisa Pilgram + 5 more
Background Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution and is often used for imaging and time series data, but there are no evaluations on its…
Liang Xiao, Jiaolong Xu, Dawei Zhao, Erke Shang + 3 more
'Alexander Wong'] Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. However, due to the non-differentiable properties of…
Wei Dai, Yanbiao Ma, Jiayi Chen, Xiaohua Chen + 2 more
'Andrea Murari'] In long-tail scenarios, models have a very high demand for high-quality data. Information augmentation, as an important class of data-centric methods, has been proposed to improve model performance by expanding the richness and quantity of samples in tail classes. However, the underlying mechanisms…
Zhifeng Li, Yaqin Song, Runchen Li, Sen Gu + 4 more
'Longchao Cao' 'Qi Zhou'] Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from…
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Background: Obstructive sleep apnea (OSA) is growing increasingly prevalent in many countries as obesity rises. Sufficient, effective treatment of OSA entails high social and financial costs for healthcare. Objective: For treatment purposes, predicting OSA patients’ visit expenses for the coming year is crucial.…