1 INTRODUCTION  
of data augmentation.  
The contributions of this paper are as follows:

2 BACKGROUND  
2.1 Large Language Models  
2.2 Diffusion Models  
2.3 Data Augmentation

3 APPROACHES  
3.1 Image Augmentation  
3.2 Text Augmentation  
3.3 Paired Data Augmentation

4 DATA POST PROCESSING  
4.1 Top-K Selection  
4.2 Model-based Approaches  
4.3 Score-based Approaches  
4.4 Cluster-based Approaches

5 APPLICATIONS  
5.1 Natural Language Processing  
5.2 Computer Vision  
5.3 Audio Signal Processing

6 SUMMARY  
6.1 The Success and Failure of Large Model-Based Data Augmentation  
6.2 Protocols and Benchmarks for Evaluation

7 GRAND CHALLENGES  
7.1 Theoretical Understanding  
7.2 The Number of Augmented Data  
7.3 Multimodal Data Augmentation  
7.4 Language and Vision Foundation Models  
7.5 Automatic Data Augmentation  
7.6 Robust and Consistent Data Augmentation  
7.7 Trustworthy Data Augmentation  
7.8 The Instruction Following Ability of Large Models  
7.9 The Evaluation of Augmented Data  
7.10 Beyond Augmentation: Training Large Models Using Augmented Data

8 CONCLUSION