Synthetic Data Augmentation

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Overview

Synthetic data augmentation studies how to create or transform training examples so models can learn from broader, more balanced, or more privacy-conscious data. Common approaches include procedural and physics-based simulation, GANs and diffusion models for images, video, speech, and sensor signals, and language models for text and structured data; simpler methods include oversampling, mixup, and targeted transformations. Work evaluates generated data not only by realism or distributional similarity, but also by label consistency, privacy, and performance on held-out real-world tasks. A central tension is that synthetic data can improve performance in scarce or underrepresented regions, yet distribution gaps, poor labels, or excessive synthetic-data ratios can leave results unchanged or make them worse.

Latest papers

CardsList

456 papers41 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

  1. What to randomize matters: a controlled study of volume, axis and representation in low-data molecular generation

    Oct 28, 2026Neerav Kaushal, Alisa Teige, Ajay MNV PenmatsaMolecular GenerationSynthetic Data Augmentation

    Department of Data Science, Michigan Technological University, 1400 Townsend Dr, Houghton, 49931, MI, USA.

  2. Structure-retained low-rank adapters for weather synthesis

    Oct 28, 2026Shunxin Wang, Alexandros Stergiou, Luuk Spreeuwers +2Image GenerationSynthetic Data Augmentation

    work done while at the University of Twente

  3. Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases

    Oct 26, 2026Hicham El Akhal, Abderrahim Waga, Aissa Ben Yahya +1Synthetic Data AugmentationPlant Disease Classification

    Computer Science and Applications Laboratory, Faculty of Sciences, Moulay Ismail University of Meknes, PB 11201 Zitoune, Meknes, 50000, Morocco . · Euromed University of Fez, Fez, Morocco .

  4. Learning Dual-Camera Smooth Zoom via Decoupled Geometric-Photometric Data Synthesis

    Oct 26, 2026Junjie Fan, Zhilu Zhang, Renlong Wu +2Video Frame InterpolationSynthetic Data Augmentation

    Computational Mathematics, Harbin Institute of Technology, Harbin, China. · Faculty of Computing, Harbin Institute of Technology, Harbin, China.

  5. Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation

    Oct 25, 2026Yuhao Qi, Wenlin Dai, Xuehu ZhuSynthetic Data AugmentationData Augmentation

    Institute of Statistics and Big Data, Renmin University of China, Beijing, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

  6. Fast and Useful: Adapting Distilled Generators for Visual Learning

    Oct 23, 2026Patryk Bartkowiak, Dominik Michels, Sören Pirk +1Diffusion Model AdaptationSynthetic Data Augmentation

    Adam Mickiewicz University · GreenMatterAI · KAUST +1

  7. Local data augmentation for imbalanced binary classification

    Oct 19, 2026Harold Gu{é}neau, Alain Celisse, Pascal DelangeClass-Imbalanced LearningData Augmentation

    SAMM · LPP,MODAL

  8. Process-Aware Event Log Augmentation for Process Prediction under Labeled Data Scarcity

    Oct 17, 2026Sjoerd van Straten, Alessandro Padella, Marwan HassaniPredictive Process MonitoringProcess Mining

    Eindhoven University of Technology, Eindhoven, The Netherlands · University of Padua, Padua, Italy · University of Wuppertal, Wuppertal, Germany

  9. MESS: Multi-Exposure Sequence Synthesis for Generalizable Image Enhancement

    Oct 17, 2026Ruodai Cui, Shuaizheng Liu, Rongyuan Wu +1Image EnhancementSynthetic Data Augmentation

    Department of Computing, The Hong Kong Polytechnic University · Qualcomm Technologies, Inc. · OPPO Research Institute

  10. Controllable Multi-Object Insertion for Detection Data Augmentation

    Oct 17, 2026Jiyang Huang, Tianwei Zhang, Xiaoqin Zhang +2Synthetic Data AugmentationData Augmentation

    Nanyang Technological University · Zhejiang University of Technology · University of the Chinese Academy of Sciences

  11. ZeroDSS: Semantic Segmentation from Class Names Alone

    Oct 17, 2026Tian Ming, Marzi Heidari, Yuhong GuoSemantic SegmentationSynthetic Data Augmentation

    School of Computer Science, Carleton University, Ottawa, Canada

  12. Synthetic Image Oversampling for Rare-Class Representation Learning in Deep Facial Phenotyping

    Oct 16, 2026Miles Liao, Zhanliang Wang, Hongzhuo Chen +1Synthetic Data AugmentationClass-Imbalanced Learning

    Hopkins Authentic Research Program for Students, Hopkins School, New Haven, CT 06515, USA · Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA · Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA +1

  13. LiveSync: Iterative Self-Training for Lip-Sync Generation

    Oct 16, 2026Huaishao Luo, Huan Zhang, Guanbo Bao +3Lip SynchronizationAudio-Driven Facial Animation

    JD Technology

  14. Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft

    Oct 15, 2026Alvaro Peiro, Marios ImpraimakisSynthetic Data AugmentationSynthetic Data Generation

    Department of Mechanical Engineering, University of Bath, Bath, BA2 7AY, UK

  15. CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis

    Oct 14, 2026Haoxiang Rao, Fang Zhao, Zhao Wang +3Synthetic Anomaly GenerationSynthetic Data Augmentation

    School of Intelligence Science and Technology, Nanjing University · China Mobile Zijin Innovation Institute

  16. Structure-Preserving Document-Level Data Augmentation via Discourse Parsing

    Oct 14, 2026Ruilin Shang, Shilong Hou, Zi Long +1Data AugmentationSynthetic Data Augmentation

    Shenzhen University, China · Shenzhen Technology University, China

  17. NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation

    Oct 14, 2026Hochul Hwang, Jahir Sadik Monon, Soowan Yang +9Visually Impaired Navigation AssistanceAssistive Technology for Blind and Low-Vision People

    University of Massachusetts Amherst · Daegu Gyeongbuk Institute of Science and Technology · Boston University

  18. FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection

    Oct 13, 2026Rashi Niyas P, Ritika Jain, Hitika TiwariFall DetectionDataset Condensation

    Indian Institute of Technology Madras · Indian Institute of Technology Guwahati

  19. ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts

    Oct 12, 2026Hossein Omid Beiki, Sepehr Asgari, Oliver Petrovic +1Synthetic Data GenerationSynthetic Data Augmentation

    Laboratory for Machine Tools and Production Engineering WZL of RWTH, Aachen University, Aachen, Germany

  20. Genesis: Visual Augmentation Primitives for Multimodal Chain-of-Thought Reasoning

    Oct 11, 2026Fan Yang, Lifeng Chen, Yipu Wang +14VLM ReasoningMultimodal CoT Reasoning

    Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Beijing Jiaotong University +4

  21. Generative Quantum Machine Learning Methods for Cancer Detection using Augmented Datasets

    Oct 10, 2026Sophia Knutson, Nandika Goyal, Gennaro De Luca +3Ultrasound Image ClassificationMedical Image Classification

    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA · SenSIP Center, Arizona State University, Tempe, AZ, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA

  22. Improving Semantic Coverage in AI Test & Evaluation through Generative Augmentation

    Oct 9, 2026Connor Hashemi, Bharadwaj Ravichandran, Connor Anderson +4Synthetic Data AugmentationGenerative AI Evaluation

    Kitware Inc, 1712 Route 9 Suite 300, Clifton Park, NY USA

  23. SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

    Oct 9, 2026Ziyu Luo, Wei Li, Haoping Huang +2Large Language Model-Based Data AugmentationFake News Detection

    Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing 401331, China · School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China · Department of Plastic Surgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China +1

  24. Adaptive Adversarial Augmentation for Controllable Face Synthesis

    Oct 8, 2026Saransh Suri, Shivang Agarwal, Mayank Vatsa +1Synthetic Data AugmentationData Augmentation

    Manipal University Jaipur, India · Indian Institute of Technology Jodhpur, India · Birla Institute of Technology and Science Pilani Dubai Campus, Dubai, United Arab Emirates

  25. Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation

    Oct 7, 2026Chunming He, Kailai Zhou, Jiaming Zuo +6Training Data CurationSynthetic Data Selection

    Tsinghua University, China · X3000 Inspection Co., Ltd · AI4X Team, Nanyang Technological University, Singapore +1

  26. Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation

    Oct 7, 2026Shunsuke Mitsumori, Tomoya Mizumoto, Yusuke FujitaSpeech Language ModelsLanguage Model Robustness

    SB Intuitions · Waseda University, Tokyo, Japan

  27. FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases

    Oct 7, 2026Hongzhuo Chen, Zhanliang Wang, Florent Pollet +9Rare Disease DiagnosisSynthetic Data Evaluation

    Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA · Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania, Philadelphia, PA, USA · Department of Biomedical Informatics, Columbia University, New York, NY, USA +6

  28. Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction

    Oct 6, 2026Hojun Lim, Hyeongseok Jeon, Donghyun Kim +2Synthetic Data AugmentationSim-to-Real Transfer

    Technical University of Munich, Germany; School of Engineering & Design, Department of Mobility Systems Engineering, Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) · MORAI Inc., Seoul, South Korea · AVP Division, Hyundai Motor Company, Gyeonggi-do, South Korea +1

  29. MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation

    Oct 5, 2026Suzannah Wistreich, Stephen Tian, Isabella Huang +4Synthetic Data AugmentationMobile Manipulation

    Stanford University · Toyota Research Institute

  30. Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data

    Oct 5, 2026Abu Talha, Peng Liu, Souradyuti PaulClass-Imbalanced LearningSynthetic Data Augmentation

    Indian Institute of Technology Bhilai, Durg, C.G. 491002, India · Loughborough University, Loughborough, LE11 3TU, United Kingdom