Feature Augmentation
Feature Augmentation is a graph feature engineering technique that expands the original node or edge features by introducing derived or synthetic attributes through deterministic or learned transformations to enhance representational capacity.
346
Publications
35.9K
Citations
1.4K
Authors
471
Institutions
Publications per year
2017–2026
321
Parents
346
A survey on Image Data Augmentation for Deep Learning
Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data · 2019 · 12.4K citations · Full text
AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mané et al. · 2019 · 2.7K citations
Data augmentation: A comprehensive survey of modern approaches
Alhassan Mumuni, Fuseini Mumuni · Array · 2022 · 800 citations · Full text
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong, Wenbing Huang, Tingyang Xu et al. · arXiv (Cornell University) · 2019 · 631 citations · Full text
AugFPN: Improving Multi-Scale Feature Learning for Object Detection
Chaoxu Guo, Bin Fan, Qian Zhang et al. · 2020 · 547 citations
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471
Tsinghua University
Beijing, China
13 h-index
36 publications
1.9K citations
National University of Singapore
Singapore, Singapore
12 h-index
40 publications
3K citations
Nanyang Technological University
Singapore, Singapore
11 h-index
24 publications
3.1K citations

Chinese Academy of Sciences
Beijing, China
10 h-index
21 publications
2.8K citations

Shanghai Jiao Tong University
Shanghai, China
9 h-index
16 publications
791 citations

Beihang University
Beijing, China
9 h-index
19 publications
2.3K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
15 publications
1.6K citations

Huazhong University of Science and Technology
Wuhan, China
7 h-index
34 publications
2.2K citations
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Repository · 37 publications · 2.6K citations
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Conference · 11 publications · 598 citations
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9 publications · 673 citations
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8 publications · 389 citations
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8 publications · 625 citations
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