Data to 7 Oct 2026

BAGAN: Data Augmentation with Balancing GAN

arXiv 1803.09655 ↗First posted 26 Mar 2018cs.CV cs.LG stat.MLcited by 90

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Abstract (arXiv)

Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial training all available images of majority and minority classes. The generative model learns useful features from majority classes and uses these to generate images for minority classes. We apply class conditioning in the latent space to drive the generation process towards a target class. The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space. We compare the proposed methodology with state-of-the-art GANs and demonstrate that BAGAN generates images of superior quality when trained with an imbalanced dataset.

Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2018.

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Citing papers per year: 2021: 17; 2022: 26; 2023: 20; 2024: 12; 2025: 11; 2026: 4.

90 papers.

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2022StyleGAN-Human: A Data-Centric Odyssey of Human Generationcited by 143
2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
2022A Comprehensive Survey of Image Augmentation Techniques for Deep Learningcited by 95
2021DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Datacited by 62
2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
2024A survey of synthetic data augmentation methods in computer visioncited by 34
2022SOS: Score-based Oversampling for Tabular Datacited by 29
2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
2021Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencodercited by 27
2021Memory-guided Unsupervised Image-to-image Translationcited by 21
2024Distribution-Aware Data Expansion with Diffusion Modelscited by 20
2021Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networkscited by 18

38 more

2021GAN-based Data Augmentation for Chest X-ray Classificationcited by 18
2022"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shiftscited by 18
2021LARGE: Latent-Based Regression through GAN Semanticscited by 17
2022SpoofGAN: Synthetic Fingerprint Spoof Imagescited by 17
2023Hallucination Improves the Performance of Unsupervised Visual Representation Learningcited by 16
2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
2021On the Fairness of Generative Adversarial Networks (GANs)cited by 14
2023Image retrieval outperforms diffusion models on data augmentationcited by 14
2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
2022Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completioncited by 13
2022Do Deep Networks Transfer Invariances Across Classes?cited by 12
2021Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regressioncited by 10
2021MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translationcited by 10
2023Rethinking Data Augmentation for Tabular Data in Deep Learningcited by 10
2024Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completioncited by 10
2025A Comprehensive Survey on Imbalanced Data Learningcited by 10
2021METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopycited by 9
2022RareGAN: Generating Samples for Rare Classescited by 9
2022RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clippingcited by 9
2022Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Imagescited by 8
2024Comparative Analysis of Generative Models: Enhancing Image Synthesis with VAEs, GANs, and Stable Diffusioncited by 7
2021Game of GANs: Game-Theoretical Models for Generative Adversarial Networkscited by 6
2022Efficient Augmentation for Imbalanced Deep Learningcited by 6
2022Scalable Adversarial Online Continual Learningcited by 6
2023A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Modelcited by 6
2023Data Optimization in Deep Learning: A Surveycited by 6
2025ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detectioncited by 6
2021Optimal Binary Classification Beyond Accuracycited by 5
2021An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classificationcited by 5
2022Conditional Generative Data Augmentation for Clinical Audio Datasetscited by 5
2022Augmentation Backdoorscited by 5
2022Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Networkcited by 5
2022Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Samplingcited by 5
2023Sharp error bounds for imbalanced classification: how many examples in the minority class?cited by 4
2023MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformationscited by 4
2021IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalancecited by 3
2022Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Reviewcited by 3
2022BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognitioncited by 3

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