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SAPDA: Significant Areas Preserved Data Augmentation
Data Augmentation is an essential technology for improving the performance of deep learning models. However, the semantic information change in...
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Novel data augmentation for named entity recognition
Named entity recognition (NER) is a crucial Natural language processing (NLP) task used in applications like voice assistants, search engines,...
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Point Cloud Data Augmentation for Linear Assets
Machine learning algorithms are creating new approaches to address issues faced by the industry. These methods are data hungry for the right quality...
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Microbial data augmentation combining feature extraction and transformer network
Microbial data exhibit high dimensionality, feature sparseness, and class imbalance. Popular data augmentation strategies typically generate...
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Data Augmentation for Improving Explainability of Hate Speech Detection
The paper presents a novel data augmentation-based approach to develop explainable, deep learning models for hate speech detection. Hate speech is...
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Application of Data Augmentation Techniques in Predicting Travel Time Reliability: Evidence from England
This study investigates the effectiveness of data augmentation techniques like noise creation, scaling, shifting, and Grey models (GMs) for improving...
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An Overview on Data Augmentation for Machine Learning
The effective utilization of data augmentation stands as a strategic imperative in the domains of industrial enterprises and economics, offering a...
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GAN-based one dimensional medical data augmentation
With the continuous development of human life and society, the medical field is constantly improving. However, modern medicine still faces many...
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Mortality Forecasting Using Data Augmentation
Recent mortality forecasting studies using artificial neural networks (ANNs) have shown improved forecasting performance compared with previous...
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Data augmentation using Heuristic Masked Language Modeling
Data augmentation has played an important role in generalization capability and performance improvement for data-driven deep learning models in...
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A novel data augmentation approach for ego-lane detection enhancement
Utilizing vast annotated datasets for supervised training of deep learning models is an absolute necessity. The focus of this paper is to demonstrate...
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SENet-based speech emotion recognition using synthesis-style transfer data augmentation
This paper addresses speech emotion recognition using a channel-attention mechanism with a synthesized data augmentation approach. Convolutional...
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Comparison of Textual Data Augmentation Methods on SST-2 Dataset
Since the arrival of advanced deep learning models, more successful techniques have been proposed, significantly enhancing the performance of nearly...
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Revitalizing Arabic Character Classification: Unleashing the Power of Deep Learning with Transfer Learning and Data Augmentation Techniques
Deep learning techniques have demonstrated remarkable success in various domains, including character classification tasks. However, the performance...
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A Highly Imbalanced Assembly Vibration Prediction of Aero-engine Using Feature Selection and Data Augmentation
PurposeIn the process of aero-engine assembly, the vibration value of the whole aero-engine has an important impact on flight safety. To limit the...
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Boosting Adversarial Training Using Robust Selective Data Augmentation
Artificial neural networks are currently applied in a wide variety of fields, and they are near to achieving performance similar to humans in many...
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Modulation classification with data augmentation based on a semi-supervised generative model
Although modulation classification with deep learning has been widely explored, this is challenging when the training data is limited. In this paper,...
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Role of Data Augmentation and Effective Conservation of High-Frequency Contents in the Context Children’s Speaker Verification System
Develo** an automatic speaker verification (ASV) system for children’s speech presents significant challenges. One major obstacle is the scarcity...
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Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling
Develo** robust methods for evaluating protein–ligand interactions has been a long-standing problem. Data-driven methods may memorize ligand and...
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Data Augmentation Using Spectral War** for Low Resource Children ASR
In low resource children automatic speech recognition (ASR) the performance is degraded due to limited acoustic and speaker variability available in...
