# A Comprehensive Survey on Data Augmentation

arXiv: https://arxiv.org/abs/2405.09591 · HTML: https://mlsanity.com/paper/2405.09591/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 171 of its 295 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 30 of 171)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021)

## Built on by (most cited first, 30 of 40)

- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?](https://mlsanity.com/paper/2502.14502/) (2025)
- [Diversity-oriented Data Augmentation with Large Language Models](https://mlsanity.com/paper/2502.11671/) (2025)
- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [Text Reinforcement for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2509.00687/) (2025)
- [scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data](https://mlsanity.com/paper/2505.12626/) (2025)
- [Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents](https://mlsanity.com/paper/2601.18217/) (2026)
- [Universal pre-training by iterated random computation](https://mlsanity.com/paper/2506.20057/) (2025)
- [Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security](https://mlsanity.com/paper/2605.23989/) (2026)
- [AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems](https://mlsanity.com/paper/2511.18151/) (2025)
- [scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing](https://mlsanity.com/paper/2512.02471/) (2025)
- [Applications of Large Language Model Reasoning in Feature Generation](https://mlsanity.com/paper/2503.11989/) (2025)
- [Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images](https://mlsanity.com/paper/2505.14730/) (2025)
- [Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures](https://mlsanity.com/paper/2506.06832/) (2025)
- [Soft Graph Clustering for single-cell RNA Sequencing Data](https://mlsanity.com/paper/2507.09890/) (2025)
- [Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation](https://mlsanity.com/paper/2509.17204/) (2025)
- [Cognitive Training for Language Models: Towards General Capabilities via Cross-Entropy Games](https://mlsanity.com/paper/2603.22479/) (2026)
- [LexPro-1.0 Technical Report](https://mlsanity.com/paper/2503.06949/) (2025)
- [Individualised Counterfactual Examples Using Conformal Prediction Intervals](https://mlsanity.com/paper/2505.22326/) (2025)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation](https://mlsanity.com/paper/2603.08605/) (2026)
- [How Data Augmentation Shapes Neural Representations](https://mlsanity.com/paper/2605.15306/) (2026)
- [Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach](https://mlsanity.com/paper/2407.03146/) (2024)
- [Adaptable Embeddings Network (AEN)](https://mlsanity.com/paper/2411.13786/) (2024)
- [The Evolution and Future Perspectives of Artificial Intelligence Generated Content](https://mlsanity.com/paper/2412.01948/) (2024)
- [LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning](https://mlsanity.com/paper/2501.04861/) (2025)
- [GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation](https://mlsanity.com/paper/2507.15577/) (2025)
- [Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring](https://mlsanity.com/paper/2507.18293/) (2025)
- [AdaR: A Framework for Equipping LLMs with Adaptive Reasoning](https://mlsanity.com/paper/2510.04617/) (2025)
- [Data-Centric Visual Development for Self-Driving Labs](https://mlsanity.com/paper/2512.02018/) (2025)

## 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.