A Comprehensive Survey on Data Augmentation
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917013934309376 |
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| author | Wang, Zaitian Wang, Pengfei Liu, Kunpeng Wang, Pengyang Fu, Yanjie Lu, Chang-Tien Aggarwal, Charu C. Pei, Jian Zhou, Yuanchun |
| author_facet | Wang, Zaitian Wang, Pengfei Liu, Kunpeng Wang, Pengyang Fu, Yanjie Lu, Chang-Tien Aggarwal, Charu C. Pei, Jian Zhou, Yuanchun |
| contents | Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, this survey proposes a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities by investigating how to take advantage of the intrinsic relationship between and within instances. Additionally, it categorizes data augmentation methods across five data modalities through a unified inductive approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Data Augmentation Wang, Zaitian Wang, Pengfei Liu, Kunpeng Wang, Pengyang Fu, Yanjie Lu, Chang-Tien Aggarwal, Charu C. Pei, Jian Zhou, Yuanchun Machine Learning Artificial Intelligence Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, this survey proposes a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities by investigating how to take advantage of the intrinsic relationship between and within instances. Additionally, it categorizes data augmentation methods across five data modalities through a unified inductive approach. |
| title | A Comprehensive Survey on Data Augmentation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.09591 |