mlsanityData to 7 Oct 2026

A Comprehensive Survey on Data Augmentation

arXiv 2405.09591 ↗First posted 15 May 2024cs.LG cs.AIcited by 38

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Abstract (arXiv)

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.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 171 of its 295 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

171 of 295 references.

References · 171

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38 more
  1. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  2. 2015Spatial Transformer Networkscited by 1,423
  3. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  4. 2017Random Erasing Data Augmentationcited by 1,235
  5. 2020Graph Contrastive Learning with Augmentationscited by 1,203
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  7. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
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  20. 2019Diffusion Improves Graph Learningcited by 414
  21. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 22; 2026: 12.

38 papers.

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  1. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
  2. 2025How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?cited by 11
  3. 2025Diversity-oriented Data Augmentation with Large Language Modelscited by 7
  4. 2025Deep Cut-informed Graph Embedding and Clusteringcited by 7
  5. 2025scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Datacited by 6
  6. 2025Text Reinforcement for Multimodal Time Series Forecastingcited by 6
  7. 2026Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agentscited by 6
  8. 2025Universal pre-training by iterated random computationcited by 5
  9. 2026Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system securitycited by 5
  10. 2025AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systemscited by 4
  11. 2025scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencingcited by 3
  12. 2025Applications of Large Language Model Reasoning in Feature Generationcited by 2
26 more
  1. 2025Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measurescited by 2
  2. 2025Soft Graph Clustering for single-cell RNA Sequencing Datacited by 2
  3. 2025Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigationcited by 2
  4. 2026Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Gamescited by 2
  5. 2025Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Imagescited by 1
  6. 2025Individualised Counterfactual Examples Using Conformal Prediction Intervalscited by 1
  7. 2025Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learningcited by 1
  8. 2026How Data Augmentation Shapes Neural Representationscited by 1
  9. 2024Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approachcited by 0
  10. 2024Adaptable Embeddings Network (AEN)cited by 0
  11. 2024The Evolution and Future Perspectives of Artificial Intelligence Generated Contentcited by 0
  12. 2025LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learningcited by 0
  13. 2025LexPro-1.0 Technical Reportcited by 0
  14. 2025GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentationcited by 0
  15. 2025Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoringcited by 0
  16. 2025AdaR: A Framework for Equipping LLMs with Adaptive Reasoningcited by 0
  17. 2025Data-Centric Visual Development for Self-Driving Labscited by 0
  18. 2025Quality-Driven and Diversity-Aware Sample Expansion for Robust Marine Obstacle Segmentationcited by 0
  19. 2026Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentationcited by 0
  20. 2026Data Augmentation via Causal-Residual Bootstrappingcited by 0
  21. 2026Generative models on phase spacecited by 0
  22. 2026AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentationcited by 0
  23. 2026Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentationcited by 0
  24. 2026MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learningcited by 0
  25. 2026Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprintcited by 0
  26. 2026Talk to Me, Jarvis: An Open-Source Edge-Deployable Voice Assistant Framework for Autonomous Racecarscited by 0