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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A Systematic Approach for Selecting Trajectories for Data Augmentation
Jun 9, 2026Adam NordlingSynthetic Data Augmentation Data Augmentation
++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation
Jun 9, 2026Ana Sofia Santos, André Ferreira, Gijs Luijten +6Synthetic Data Augmentation Data Augmentation
Center Algoritmi / LASI, University of Minho, Braga, Portugal · Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany · Institute of Medical Informatics / Dept. of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen,2026 Germany +1
Optical Music Recognition for Real-World Manuscripts with Synthetic Data
Jun 8, 2026Jiří Mayer, Martina Dvořáková, Vojtěch Dvořák +6Optical Music Recognition Synthetic-to-Real Domain Adaptation
Institute of Formal and Applied Linguistics, Charles University · Moravian Library
SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
Jun 7, 2026Hongyi Yu, Yaoyu Fang, Jiahe Qian +3Synthetic Data Augmentation Spatial Transcriptomics
Northwestern University, Department of Radiology, Chicago, IL, USA · Yale University, Department of Biostatistics, New Haven, CT, USA · Northwestern University, Department of Cell and Developmental Biology, Chicago, IL, USA +1
Synesthesia via Direct Latent Augmentation:Bypassing the Decode-Encode Loop for Cross-Modal Distillation
Jun 6, 2026Cristian Sbrolli, Nicolas Michel, Matteo Matteucci +1Cross-Modal Knowledge Distillation Synthetic Data Augmentation
Politecnico di Milano, Milan, Italy · University of Tokyo, Tokyo, Japan
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
Jun 6, 2026Mattia Forlesi, Alfonso Esposito, Ivan Zyrianoff +2Synthetic Data Augmentation Synthetic Data Generation
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy · Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Bologna, Italy
Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation
Jun 5, 2026Patrick Kage, Trevor Hedges, N. Siddharth +1Contrastive Learning Synthetic Data Augmentation
School of Informatics The University of Edinburgh Edinburgh, Scotland EH8 9AB UK · Massachusetts Institute of Technology Lincoln Laboratory Lexington, MA 02421 USA
Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios
Jun 5, 2026Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli +2Synthetic Data Augmentation Medical Image Anomaly Detection
Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Jun 4, 2026Yohann Benchetrit, Marlène Careil, Simon Dahan +3Synthetic Data Augmentation Neural Decoding
Meta AI
LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations
Jun 4, 2026Mritula Chandrasekaran, Sanket Kachole, Jarek Francik +1Synthetic Data Augmentation Synthetic Data Generation
School of Computer Science and Mathematics, Kingston University, London, United Kingdom
ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition
Jun 4, 2026Pablo Ayuso-Albizu, Pablo Carballeira, Juan C. SanMiguel +1Synthetic Data Augmentation Diffusion-Based Image Generation
Universidad Aut´onoma de Madrid, Madrid, Spain
Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
Jun 4, 2026Bin Liu, Jun Wu, Haoyu Peng +4Class-Imbalanced Learning Synthetic Data Augmentation
Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, China · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China · State Key Laboratory of Novel Software Technology, Nanjing University, China +1
Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation
Jun 4, 2026Shawaiz Obaid, Nida Chandio, Neha Jamil +1Class-Imbalanced Learning Synthetic Data Augmentation
School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad, Pakistan
Efficient ASR Training with Conversations that Never Happened
Jun 2, 2026Máté Gedeon, Péter MihajlikSynthetic Data Augmentation Automatic Speech Recognition
Dept. of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, Hungary · SpeechTex Ltd., Budapest, Hungary · ELTE Research Centre for Linguistics, Budapest, Hungary
Trading Human Curation for Synthetic Augmentation in RLVR
Jun 2, 2026Akshansh, Leonardo Rosa Rodrigues, Michael Korostelev +2Synthetic Data Augmentation Reinforcement Learning with Verifiable Rewards
Pareto AI
FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
Jun 2, 2026Chang Kong, Yuebing Li, Peng Mo +2Synthetic Data Augmentation Vision-Language Models
Shenzhen Polytechnic University, Shenzhen, China · Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong Macao2026 Greater Bay Area, Shenzhen, China · Shenzhen University, China
Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation
Jun 1, 2026Ni Li, Nuohao Liu, Ryan Jacobs +5Synthetic Data Augmentation Latent Diffusion Models
University of Wisconsin-Madison, Madison, WI · University of Michigan-Ann Arbor, Ann Arbor, MI
FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis
Jun 1, 2026Zhimin Li, Ruichen Zhang, Zhen Tan +3Diffusion Model Alignment Synthetic Data Augmentation
University of Pittsburgh, Swanson School of Engineering, Pittsburgh, 15261, USA · 2The University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, 27599, USA · 3Arizona State University, School of Computing and Augmented Intelligence, Tempe, 85281, USA +1
C2GA: A Class-Controllable Generative Augmentation Framework for Respiratory Sound Classification
Jun 1, 2026Ziqi Ma, Mengyu Han, Anteng Cai +4Synthetic Data Augmentation Audio Classification
aSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China · bSchool of AI and Advanced Computing (AIAC), XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Taicang, Suzhou, 215400, China · cISIR, Osaka University, Suita, 567-0047, Osaka, Japan
Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection
Jun 1, 2026Atmika Bhardwaj, Silvia Vock, Nico SteckhanSynthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Federal Institute for Occupational Safety and Health (BAuA), Germany
Data Enrichment for Symbolic Regression Using Diffusion Models
May 31, 2026Simon De Reuver, Tamas Kristof Toth, Teddy LazebnikPhysics-Informed Diffusion Models Physics-Informed Generative Modeling
Department of Computing, Jönköping University, Jönköping, Sweden · Department of Information Science, University of Haifa, Haifa, Israel
State Machine Guided Multi-Relational Synthetic Data from Logs for Anomaly Detection
May 30, 2026Aja Khanal, Apurva NarayanSynthetic Data Augmentation Online Anomaly Detection
University of Western Ontario London, Canada
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
May 29, 2026Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12Synthetic Data Augmentation Synthetic Data Generation
Meta
SAM for Robust Mitochondria Instance Segmentation in Fluorescence Microscopy
May 29, 2026Suyog Jadhav, Dilip K. Prasad, Krishna AgarwalSynthetic Data Augmentation Instance Segmentation
UiT The Arctic University of Norway
GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
May 29, 2026Xiangtao Kong, Jixin Zhao, Lingchen Sun +2Synthetic Data Augmentation Image Restoration
The Hong Kong Polytechnic University · OPPO Research Institute
Iterative Framework For Data Augmentation Of Segmented Fingerprints
May 29, 2026João Leonardo H. D. Agnol, Wesley Augusto de Bona, Erick Oliveira Rodrigues +4Synthetic Data Augmentation Biometric Identification
Federal University of Technology (UTFPR) Pato Branco, Parana Brazil · InfantID Curitiba, Parana, Brazil
Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models
May 28, 2026Arunkumar Kannan, Yanbo Zhang, Han Liu +5Synthetic Data Augmentation Latent Diffusion Models
aDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA · bJohns Hopkins University, Baltimore, MD, USA · cDigital Technology and Innovation, Siemens Healthineers, Erlangen, Germany +1
GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
May 28, 2026Jiacong Liu, Shu Luo, Yikai Qin +3Synthetic Data Augmentation Few-Shot Domain Adaptation
Huazhong University of Science and Technology
Representation-Conditioned Diffusion Models for Guided Training Data Generation
May 26, 2026Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel EilertsenSynthetic Data Augmentation Latent Diffusion Models
Linkoping University
Personalized Generative Models for Contextual Debiasing
May 25, 2026Xinran Liang, Esin Tureci, Prachi Sinha +3Synthetic Data Augmentation T2I Generation
Department of Computer Science, Princeton University · LIX, CNRS, ´Ecole Polytechnique

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A Systematic Approach for Selecting Trajectories for Data Augmentation

Adam Nordling

Synthetic Data Augmentation Data Augmentation Training Data Selection Time Series Data Augmentation

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Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive random selection, leaving a critical gap in understanding which trajectories should be augmented for maximal benefit. This thesis addresses this gap by developing a systematic and scalable framework to evaluate five systematic selection strategies: Outlierness, Diversity, Representativeness, Uncertainty, and Random selection. These strategies were rigorously tested across four datasets covering animal behavior (Foxes and Starkey), maritime traffic (AIS), and urban traffic (Car) using a suite of linear and non-linear machine learning models. As part of this evaluation, an Optuna-based hyperparameter optimization loop was integrated to empirically identify the best-performing augmentation parameters for each dataset within the explored search space. The results indicate that, while systematic selection is not a universal solution, it offers distinct advantages over the random baseline. Systematic strategies, particularly Outlierness and Uncertainty, demonstrated higher stability and were less prone to performance degradation observed with random sampling in dense datasets. However, the findings also reveal that the value of augmentation is strictly conditional. Visual analysis via UMAP demonstrates that while systematic augmentation successfully repairs topological fragmentation in sparse datasets, it can act as a corrupting noise signal in high-quality, dense datasets. Furthermore, the study identified physical limitations in high-velocity domains, where standard perturbation techniques lead to divergence in feature space...

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