Synthetic Data Augmentation

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24 papers in the last four weeks, up 85% on the four weeks before. 0.2% of all new papers.

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Hard, Yet Reducible: Controlled Forward Transfer for Synthetic Degradation Curation
Oct 7, 2026Chunming He, Kailai Zhou, Jiaming Zuo +6Training Data Curation Synthetic Data Selection
Tsinghua University, China · X3000 Inspection Co., Ltd · AI4X Team, Nanyang Technological University, Singapore +1
Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation
Oct 7, 2026Shunsuke Mitsumori, Tomoya Mizumoto, Yusuke FujitaSpeech Language Models Language Model Robustness
SB Intuitions · Waseda University, Tokyo, Japan
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 Diagnosis Synthetic 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
Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction
Oct 6, 2026Hojun Lim, Hyeongseok Jeon, Donghyun Kim +2Synthetic Data Augmentation Sim-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
MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation
Oct 5, 2026Suzannah Wistreich, Stephen Tian, Isabella Huang +4Synthetic Data Augmentation Mobile Manipulation
Stanford University · Toyota Research Institute
Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data
Oct 5, 2026Abu Talha, Peng Liu, Souradyuti PaulClass-Imbalanced Learning Synthetic Data Augmentation
Indian Institute of Technology Bhilai, Durg, C.G. 491002, India · Loughborough University, Loughborough, LE11 3TU, United Kingdom
Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing
Oct 5, 2026Ashik E Rasul, Hyung-Jin YoonAerial Robotics Synthetic Data Augmentation
Department of Mechanical and Nuclear Engineering Tennessee Technological University Cookeville, USA
Graph Data Augmentation via Contrastive Generator Inversion ( $\texttt{DCBA}$ )
Oct 5, 2026Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4Synthetic Data Augmentation Data Augmentation
Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics +1
Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings
Oct 1, 2026Yuan Cao, Sumeet Dash, Antonia Zachariadis +3Synthetic Data Augmentation Medical Image Segmentation
Department Artificial Intelligence in Biomedical Engineering (AIBE), Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Nürnberger Strasse 74, 91052 Erlangen, Germany · Otto von Guericke University Magdeburg, Medical Faculty, Department of Neurology, Leipziger Straße 44, 39120 Magdeburg, Germany · German Centre for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany +1
Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting
Oct 1, 2026Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan +3Synthetic Data Augmentation Diffusion Model Guidance
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281 USA
After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data
Sep 29, 2026Shilin Hu, Jingyi Xu, Dimitris Samaras +1Synthetic Data Augmentation Shadow Removal
Department of Computer Science Stony Brook University · Department of Computer Science University of North Carolina at Charlotte
From Unity Simulation to Diffusion-Based Augmentation: Quantifying Dataset Balance for Robust Object Detection
Sep 29, 2026Mohamed Benkedadra, Aissa Saoudi, Maxime Gloesener +2Synthetic-to-Real Domain Adaptation Synthetic Data Augmentation
ILIA, Université de Mons Mons, Belgium · Université Polytechnique Hauts-de-France Valenciennes, France · DeepILIA, ILIA, Université de Mons Mons, Belgium +1
Learning from synthetic photorealistic raindrop for single image raindrop removal
Sep 29, 2026Zhixiang Hao, Shaodi You, Yu Li +2Synthetic Data Augmentation Physically Based Rendering
State Key Laboratory of VR Technology and Systems, Beihang University, Beijing, China · 61-CSIRO · Tencent +2
PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
Sep 29, 2026Levente Lippenszky, Hongxu Yang, Marcell Dömötör +2Synthetic Data Augmentation Latent Diffusion Models
GE HealthCare
HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing
Sep 29, 2026Li Pang, Xinqiao Wu, Jing Yao +5Synthetic Data Augmentation Vision Foundation Model Adaptation
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China · State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China +4
FLASH: A "Generate Once, Synthesize Many" Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection
Sep 29, 2026Abhay Kumar Das, Rajesh Gangireddy, Ashwin Vaidya +1Synthetic Data Augmentation Industrial Anomaly Detection
Silicon University, Bhubaneswar, India · Intel
Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark
Sep 28, 2026Marco Parola, Mario G. C. A. Cimino, Sabrina SenatoreSynthetic Data Augmentation Medical Imaging
Aalborg University, Denmark. · University of Pisa, Italy. · University of Salerno, Italy.
Style-Driven Data Synthesis and Degradation-Aware Enhancement for Ultrasound Image Restoration
Sep 28, 2026Yu-Kai Wang, Chun-Xin Tan, Manh-Hung Nguyen +1Synthetic Data Augmentation Low-Rank Adaptation
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan · Ho Chi Minh City University of Technology and Engineering, Ho Chi Minh City, Vietnam
Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
Sep 27, 2026Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar +1Synthetic Data Augmentation Synthetic Data Generation
University of Bayreuth, AI Responsibility Centre, and Fraunhofer FIT, Universitätstraße 30, 95447 Bayreuth, Germany · Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
Structured Pose-Conditioned Flow Matching for Generative 5G CSI Augmentation
Sep 24, 2026Haojin Li, Anbang Zhang, Wai Ho Mow +3Flow Matching Synthetic Data Augmentation
University of Science and Technology Beijing, China · Department of ECE, The Hong Kong University of Science and Technology, Hong Kong · University of Exeter +1
Beyond the Foreground: FOV-Aware Polyp Image Synthesis via Lesion-Guided Adaptive Mucosal Context Propagation
Sep 17, 2026Tong Wang, Yuting He, Bin Ren +2Synthetic Data Augmentation Diffusion-Based Image Generation
Southeast University · MBZUAI · Case Western Reserve University
PDA++: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing
Sep 16, 2026Xianchi Dong, Yingyan Hou, Chao Ren +6Remote Sensing Image Understanding Synthetic Data Augmentation
A GAN-Based Framework for Robust DDoS Attack Detection
Sep 16, 2026Makram Chehayeb, Walid Fahs, Amina Rizk +2Adversarial Training Synthetic Data Augmentation
Islamic University of Lebanon, Faculty of Engineering, Lebanon · Lebanese University, Faculty of Sciences, Lebanon · Polytechnic Institute of Paris, T´el´ecom Paris, LTCI, France
REPAIR: Resolving Long-Tail Confusion in Scientific Retrievers via Fact-Verified Iterative Refinement
Sep 16, 2026Yerim Oh, Gunhee KimSynthetic Data Augmentation Information Retrieval
Seoul National University
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
Sep 14, 2026Rafael Pina, Varuna De Silva, Mindula IlleperumaSynthetic Data Augmentation Time Series Generation
Institute for Digital Technologies Loughborough University London London, United Kingdom
SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation
Sep 14, 2026Lila Cunge, Yuehong Liu, Hang Xu +5Synthetic Data Augmentation Medical Imaging Foundation Models
Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles, Los Angeles, CA, USA. · Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. · Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA. +1
An End-to-End Automated Pipeline for Controllable Crack Data Synthesis
Sep 11, 2026Conghui Li, Muxin Pu, Chern Hong Lim +2Synthetic Data Augmentation Learning with Noisy Labels
School of Information Technology, Monash University, Selangor, Malaysia · Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Engineering, Monash University, Selangor, Malaysia
Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study
Sep 11, 2026Yan Hon Michael Chung, Hanlin WangSynthetic Data Augmentation Low-Resource Language Processing
Division of Humanities, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR · The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR
Multimodal Taxonomic Conditioning for Generative Plankton Imagery
Sep 10, 2026Daniela Ivanova, Ozgu Goksu, Nicolas PugeaultContrastive Learning Synthetic Data Augmentation
University of Glasgow, Glasgow, Scotland, UK · National Defence University, Istanbul, Türkiye
SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking
Sep 9, 2026Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis +1Synthetic Data Augmentation Graph Generation
Federal University of São Carlos, São Carlos, São Paulo, Brazil · Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

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

Chunming He, Kailai Zhou, Jiaming Zuo, Hanqi Liu+5

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

Training Data Curation Synthetic Data Selection Synthetic Data Augmentation

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Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite training budget. Clean and degraded twins share content and labels, suggesting a score based on how much short training reduces the excess error caused by degradation. However, this gap can also shrink when clean performance deteriorates. Measuring the improvement on degraded images alone avoids that confound, but it still credits progress that the same amount of clean training would have produced. We propose the \textbf{controlled Reducible Degradation Gap} (cRDG) for regions defined by degradation type and severity. From a common checkpoint, cRDG runs two budget-matched probes that differ only in one augmentation slot, which holds either a synthetic degradation or a clean augmentation. The score is the gain on held-out degraded images relative to the clean-control probe. Clean harm is a separate feasibility constraint. cRDG reveals a correctable severity band in which training on the degradation yields high controlled gain under the available budget, and the band moves with the predictor, the starting checkpoint, and the training budget. \textbf{Curation of Reducible Bands} (\method) uses cRDG to select synthetic data without changing the predictor. On semantic segmentation and salient object detection, \method{} improves representative predictors under matched synthetic-data budgets and training schedules, extends to existing data-generation pipelines, and preserves clean performance. Code and supporting materials will be publicly released.

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