Data Augmentation

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Data augmentation creates modified or synthetic training examples so models can learn from limited, imbalanced, or varied data. Common approaches include geometric and appearance transforms, Mixup and CutMix, graph and feature perturbations, and examples generated with diffusion models, language models, or simulation. Recent work increasingly adapts augmentation to a model’s learning state and to domain-specific constraints in settings such as medical imaging, speech, and robotics. The central tension is to add useful diversity without changing labels or important structure; effectiveness depends on the task and should be tested for out-of-domain performance, calibration, and synthetic-data quality as well as accuracy.

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249 papers29 papers in the last four weeks, up 21% on the four weeks before. 0.2% of all new papers.

Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
Sep 25, 2025Dongkyu Cho, Miao Zhang, Rumi ChunaraSynthetic Data Augmentation Data Augmentation
Department of Computer Science, New York University, New York, NY, United States · Department of Biostatistics, School of Global Public Health, New York University, New York, NY, United States
Uncertain but Useful: Leveraging CNN Training Variability into Data Augmentation
Sep 5, 2025Inés Gonzalez-Pepe, Vinuyan Sivakolunthu, Yohan Chatelain +1Image Segmentation Data Augmentation
Department of Computer Science Concordia University Montreal, QC H3G 1M8, Canada · Department of Computer Science Concordia University2026 Montreal, QC H3G 1M8, Canada · Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health Toronto, ON M5T 0S8, Canada +1
Improving Mixup Calibration with Wasserstein Distributionally Robust Optimization
Jun 22, 2025Jiaming Hu, Yeping Jin, Debarghya Mukherjee +1Image Corruption Robustness Data Augmentation
Department of Mathematics & Statistics Boston University · Department of Electrical and Computer Engineering, Division of Systems Engineering Department of Biomedical Engineering Faculty of Computing & Data Sciences Boston University
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
May 30, 2025Abdul-mojeed Olabisi Ilyas, Adeleke Maradesa, Jamal Banzi +3Image-to-Image Translation Data Augmentation
Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China · Department of Informatics, Sokoine University of Agriculture, Chuo Kikuu Morogoro, Tanzania · Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China +5
Facial Recognition Leveraging Generative Adversarial Networks
May 17, 2025Zhongwen Li, Zongwei Li, Xiaoqi LiFace Recognition Data Augmentation
School of Cyberspace Security, Hainan University, Haikou, China
Batch Augmentation with Unimodal Fine-tuning for Multimodal Fusion of Large Language Models
May 10, 2025H M Dipu Kabir, Subrota Kumar Mondal, Mohammad Ali MoniMultimodal Fusion Multimodal Classification
AI and Cyber Futures Institute, Charles Sturt University, Australia · Rural Health Research Institute, Charles Sturt University, Australia · School of Computer Science and Engineering, Macau University of Science and Technology, Macao
ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions
Mar 12, 2025Tobias Christian Nauen, Brian Moser, Federico Raue +2Distribution Shift Robustness Synthetic Data Augmentation
RPTU University Kaiserslautern-Landau, Kaiserslautern, Germany · German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
Mar 7, 2025Juan Miguel Valverde, Maja Østergaard, Adrian Rodriguez-Palomo +4Topology-Preserving Image Segmentation Thin-Structure Segmentation
DTU Compute, Technical University of Denmark, Denmark · A.I. Virtanen Institute, University of Eastern Finland, Finland · Department of Chemistry and iNANO, Aarhus University, Denmark
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
Apr 25, 2022Minghui Chen, Cheng Wen, Feng Zheng +2Synthetic Data Augmentation Image Corruption Robustness
Department of Computer Science and Engineering, Southern University of Science and Technology · The University of Sydney · JD Explore Academy, JD.com Inc +1

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Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

Dongkyu Cho, Miao Zhang, Rumi Chunara

Department of Computer Science, New York University, New York, NY, United States · Department of Biostatistics, School of Global Public Health, New York University, New York, NY, United States

Synthetic Data Augmentation Data Augmentation LLM Hallucination Mitigation Multi-LLM Collaboration Medical LLMs

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Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs) have demonstrated strong generative capabilities for this purpose, their applications in high-stakes domains like healthcare present unique challenges due to the risk of generating clinically incorrect or misleading information. In this work, we propose a novel query-based model collaboration framework that integrates expert-level domain knowledge to guide the augmentation process to preserve critical medical information. Compared to existing LLM-based and traditional augmentation methods, our generated data significantly improves preservation of critical medical information and reduces hallucinations at both the token and concept levels. Experiments on downstream clinical prediction tasks demonstrate consistent performance gains over existing augmentation methods. This lightweight collaborative framework addresses the gap between LLM augmentation potential and the safety requirements of specialized domains.