Data to 7 Oct 2026

Data Augmentation Using GANs

arXiv 1904.09135 ↗First posted 19 Apr 2019cs.LG stat.MLcited by 58

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Abstract (arXiv)

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained using the training data generated by the GAN reached the same, (and surprisingly sometimes better), accuracy and recall than a DT trained on the original data set.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 15; 2023: 13; 2024: 6; 2025: 8; 2026: 5.

58 papers.

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2023Effective Data Augmentation With Diffusion Modelscited by 282
2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
2021GAN-Supervised Dense Visual Alignmentcited by 61
2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
2024Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Modelscited by 32
2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
2022Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentationcited by 19
2023CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Imagescited by 19
2021LARGE: Latent-Based Regression through GAN Semanticscited by 17
2021On the Fairness of Generative Adversarial Networks (GANs)cited by 14

38 more

2021GANORCON: Are Generative Models Useful for Few-shot Segmentation?cited by 13
2021Adversarial Imitation Learning with Trajectorial Augmentation and Correctioncited by 11
2022GenLabel: Mixup Relabeling using Generative Modelscited by 10
2022RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clippingcited by 9
2023From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applicationscited by 9
2024Personalized Representation from Personalized Generationcited by 9
2021Synthetic Data Generation for Fraud Detection using GANscited by 7
2021InvGAN: Invertible GANscited by 7
2023DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentationcited by 7
2023Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectivescited by 6
2021Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understandingcited by 4
2023Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabiccited by 4
2025DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Modelscited by 4
2022FROTE: Feedback Rule-Driven Oversampling for Editing Modelscited by 3
2024SynDiff-AD: Improving Semantic Segmentation and End-to-End Autonomous Driving with Synthetic Data from Latent Diffusion Modelscited by 3
2021Overcoming limited battery data challenges: A coupled neural network approachcited by 2
2022Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flowcited by 2
2022Semi-Supervised Learning for Mars Imagery Classification and Segmentationcited by 2
2022Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approachescited by 2
2023Data Augmentation for Modeling Human Personality: The Dexter Machinecited by 2
2024Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Mangacited by 2
2025SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regressioncited by 2
2021On Automatic Data Augmentation for 3D Point Cloud Classificationcited by 1
2022Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimescited by 1
2022Adversarial synthesis based data-augmentation for code-switched spoken language identificationcited by 1
2022An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Designcited by 1
2023WSAM: Visual Explanations from Style Augmentation as Adversarial Attacker and Their Influence in Image Classificationcited by 1
2025Quantifying the uncertainty of model-based synthetic image quality metricscited by 1
2025In-Context Bias Propagation in LLM-Based Tabular Data Generationcited by 1
2022RENs: Relevance Encoding Networkscited by 0
2022Automated Classification of Nanoparticles with Various Ultrastructures and Sizescited by 0
2022Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptationcited by 0
2023Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)cited by 0
2023Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis Detectioncited by 0
2023Analisis Eksploratif Dan Augmentasi Data NSL-KDD Menggunakan Deep Generative Adversarial Networks Untuk Meningkatkan Performa Algoritma Extreme Gradient Boosting Dalam Klasifikasi Jenis Serangan Sibercited by 0
2024Learning Run-time Safety Monitors for Machine Learning Componentscited by 0
2024Emotion Classification of Children Expressionscited by 0
2025Enhanced Smart Contract Reputability Analysis using Multimodal Data Fusion on Ethereumcited by 0

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