Data Augmentation
Data Augmentation is a data transformation technique that expands the effective size and diversity of a dataset through the application of domain-specific, rule-based, or learned perturbations to existing data instances, thereby improving model generalization and mitigating overfitting in machine learning contexts.
6.9K
Publications
516.1K
Citations
25K
Authors
4.3K
Institutions
Publications per year
2017–2026
6.4K
6.9K
A survey on Image Data Augmentation for Deep Learning
Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data · 2019 · 12.4K citations · Full text
Analysis of Incomplete Multivariate Data
David E. Booth, Joseph L. Schafer · Technometrics · 2000 · 5.7K citations
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Daniel Park, William Chan, Yu Zhang et al. · 2019 · 3.5K citations · Full text
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4.3K
Google (United States)
Mountain View, United States
52 h-index
350 publications
85.2K citations

Chinese Academy of Sciences
Beijing, China
50 h-index
500 publications
30.8K citations

Shanghai Jiao Tong University
Shanghai, China
44 h-index
326 publications
20.3K citations
Tsinghua University
Beijing, China
43 h-index
310 publications
19.5K citations
University of Chinese Academy of Sciences
Beijing, China
43 h-index
314 publications
24.5K citations

Harbin Institute of Technology
Harbin, China
36 h-index
193 publications
18.3K citations

Zhejiang University
Hangzhou, China
35 h-index
188 publications
9.6K citations
Nanyang Technological University
Singapore, Singapore
35 h-index
130 publications
12.2K citations
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Repository · 749 publications · 56.4K citations
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250 publications · 15.9K citations
64
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142 publications · 12.2K citations
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124 publications · 5.5K citations
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Conference · 87 publications · 7.9K citations
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