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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Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting
Jul 24, 2026Felipe Nunes Carbone de Carvalho, Joyce de Morais Souza, Alan de Aguiar +2Domain Randomization 3D Gaussian Splatting
Federal University of ABC (UFABC), Santo Andr´e, Brazil
Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloning
Jul 24, 2026Roseline Polle, Owen Parsons, George Fairs +5Synthetic Data Augmentation Speech Processing
thymia, UK · The University of Edinburgh, UK · University of Southampton, UK
Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping
Jul 23, 2026Logan RobbinsSynthetic Data Augmentation Temporal Consistency in Video Generation
Independent researcher.
Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement
Jul 22, 2026Matei Moldoveanu, Alain Sirois, Claire Ben Ali +2Synthetic Data Augmentation MI Classification
Naqi Logix, Paris, France · Inria Center at Univ. Bordeaux / LaBRI, Talence, France · LITIS, INSA Rouen-Normandy, France
Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n
Jul 21, 2026Ali Behbahani, Newsha Javanmardi, Shahriar Ahmed +1Synthetic Data Augmentation Object Detection
University of Tennessee, Knoxville, USA
Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation
Jul 21, 2026Sonia Cromp, Satya Sai Srinath Namburi GNVV, Youran Wang +4Synthetic Data Augmentation Video Diffusion Models
University of Wisconsin-Madison · GE HealthCare · University of Georgia +2
InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation
Jul 21, 2026Khawar Islam, Arif Mahmood, Xin Jin +1Synthetic Data Augmentation Data Augmentation
The University of Melbourne, Melbourne, Australia · The Information Technology University, Lahore, Pakistan · Westlake University, China
Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance
Jul 21, 2026Alexis Lazanas, Georgios KampouropoulosClass-Imbalanced Learning Synthetic Data Augmentation
Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece
Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty
Jul 21, 2026Yiheng Wu, Jue Hou, Roman YangarberSynthetic Data Augmentation Automated Evaluation
University of Helsinki, Finland
SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition
Jul 21, 2026Patryk Bartkowiak, Jakub Markil, Bartosz Kotrys +3Synthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Adam Mickiewicz University · ArtiCollect · KAUST +1
SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift
Jul 20, 2026Jiaqi Zhu, Xincheng Chen, Yuncheng Wu +2Synthetic Data Augmentation Generative Modeling
National University of Singapore · Renmin University of China · Beijing Institute of Technology +1
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
Jul 20, 2026David Rannaleet, Victor Gunnarsson, Bo Bernhardsson +2Synthetic Data Augmentation Neural Decoding
Department of Automatic Control, Lund University, Lund, Sweden. · Eriksholm Research Centre, Snekkersten, Denmark. · Department of Electrical Engineering, Linköping University, Linköping, Sweden.
DADIR: Density-Aware Data-level Imbalanced Regression Framework
Jul 19, 2026Shermin Shahbazi, Hossein Mohammadi, Mohsen AfsharchiSynthetic Data Augmentation Synthetic Data Generation
Department of Computer Engineering, University of Zanjan, Zanjan, Iran.
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
Jul 17, 2026Marios Tyrovolas, Argiris Sofotasios, Dimitris Metaxakis +4Class-Imbalanced Learning Synthetic Data Augmentation
Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece · Industrial Systems Institute, Athena Research Center, Patras, Greece · Archimedes Unit, Athena Research Center, Athens, Greece +3
Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection
Jul 17, 2026Raihan Sultan Pasha Basuki, Aliyah KurniasihSynthetic Data Augmentation Transformer
Department of Computer Science, Universitas Ary Ginanjar, Menara 165, Cilandak No.kav 1 Lt 2 & 18, Jakarta Selatan, 12560, DKI Jakarta, Indonesia
Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
Jul 15, 2026Thang-Anh-Quan Nguyen, Moussab Bennehar, Luis Guillermo Roldao Jimenez +5Synthetic Data Augmentation Image-to-Image Translation
Noah’s Ark, Huawei Paris Research Center, France · COSYS, Gustave Eiffel University, France · LASTIG, IGN-ENSG, Gustave Eiffel University, France
When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
Jul 15, 2026Na Li, Boyu Kuang, Hongsheng Hu +4Synthetic Data Augmentation Membership Inference Attacks
Nanjing University of Science and Technology, China · The University of Newcastle, Australia · Southeast University, China +2
Improving Medical Image Generative Models with Fréchet Distance Loss
Jul 14, 2026Andrew Marshall, Xuanang Xu, Xiaoran Zhang +3Synthetic Data Augmentation Distribution Matching
Department of Biomedical Engineering, Yale University · Department of Radiology & Biomedical Imaging, Yale University · Department of Electrical Engineering, Yale University
Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Jul 14, 2026Héctor Carrión, Narges NorouziSkin Lesion Classification Synthetic Data Augmentation
University of California, Santa Cruz, USA · University of California, Berkeley, USA
Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jul 14, 2026Jeeyung Kim, Erfan Esmaeili, Qiang QiuSynthetic Data Augmentation Diffusion Model Fine-Tuning
Purdue University, West Lafayette, USA
Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Jul 13, 2026Kuniko Paxton, Amila Akagić, Koorosh Aslansefat +2Synthetic Data Augmentation Synthetic Data Evaluation
School of Digital and Physical Science, University of Hull, Cottingham Road, Hull, HU6 7RX, United Kingdom · Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne bb, Sarajevo, 71000, Bosnia and Herzegovina
TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models
Jul 13, 2026Yuvraj Sehgal, Sneh Patel, Mahsa Panahandeh +2Synthetic Data Augmentation Synthetic Data Generation
Department of Computer Science, Brock University St. Catharines, Ontario, Canada · School of Electrical Engineering and Computer Science, University of Ottawa Ottawa, Ontario, Canada · Ciena Corporation Ottawa, Canada
Generative Augmentation of Raman Spectra for Glioma Classification
Jul 11, 2026Andrei Iuşan, Iulian Vasile, Daria Voiculescu +4Synthetic Data Augmentation Raman Spectroscopy
National Institute of Research and Development for Biological Sciences, Romania · Department of Mathematics and Statistics, University of Turku, Finland · Research Institute for Artificial Intelligence “Mihai Dragănescu”, Romanian Academy +2
The Effects of Synthetic Data and Label Distribution on Canola Branch Counting
Jul 10, 2026Amirsalar Darvishpour, Mikolaj Cieslak, Adam RunionsSynthetic Data Augmentation Plant Phenotyping
Department of Computer Science, University of Calgary
YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation
Jul 10, 2026Jaekyun Ko, Byung Wan Lim, Soomin Lee +2Synthetic Data Augmentation Image Denoising
Department of Computer Science, Hanyang University · Mobile Experience (MX) Division, Samsung Electronics
RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
Jul 10, 2026Yanxuan Yu, Dong Liu, Shu Wang +7Class-Imbalanced Learning Synthetic Data Augmentation
Columbia University · University of California, Los Angeles · University of Illinois Urbana-Champaign +3
TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation
Jul 9, 2026Hyeonseop Song, Seokhun Choi, Hoseok DoSynthetic Data Augmentation Text-Guided Image Editing
AI Lab, CTO Division, LG Electronics, Republic of Korea
SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation
Jul 8, 2026Nguyen Duy Hieu, Dang Hoai Nam, Pham Hoang Giap +2Synthetic Data Augmentation Image Generation
University of Information Technology, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · AJ Technologies, Nagoya, Japan +1
Multi-Conditioned Diffusion Synthesis of Sand Boils for Low-Resource Earthen-Levee Inspection
Jul 8, 2026Padam Jung Thapa, Abdullah Bin Naeem, Ayon Dey +2Synthetic Data Augmentation Conditional Image Generation
University of Louisiana at Lafayette, Lafayette, LA, USA · Department of Computer Science, Louisiana State University New Orleans, New Orleans, LA, USA
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
Jul 7, 2026Sangwoo Lee, Sunghwan Park, Jaewoo LeeSynthetic Data Augmentation Data Augmentation
Chung-Ang University, Seoul, South Korea

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Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting

Felipe Nunes Carbone de Carvalho, Joyce de Morais Souza, Alan de Aguiar, Charles Morphy D. Santos+1

Federal University of ABC (UFABC), Santo Andr´e, Brazil

Domain Randomization 3D Gaussian Splatting Synthetic Data Augmentation

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Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes. For complex organic subjects, such as insect specimens, extracting and rendering textured meshes is challenging. To address this issue, we propose a meshless DR framework that operates on the parameter space of 3D Gaussian Splatting (3DGS). Our method employs two independent perturbation pipelines to synthesize randomized training datasets. First, a Photometric DR pipeline alters the baked illumination and color balance by modulating the Spherical Harmonics (SH) coefficients. Second, a Procedural DR pipeline isolates the subject's geometric shape by replacing its original textures with 3D spatial noise. Finally, these perturbed radiance fields are composited over stochastically varied backgrounds using a rasterization engine. Our parameter manipulation provides a meshless alternative for generating robust datasets for complex geometries.

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Latest papers 294