Data Augmentation

Ask about this topic

Overview

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.

Latest papers

CardsList

248 papers26 papers in the last four weeks, down 10% on the four weeks before. 0.1% of all new papers.

  1. CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition

    Oct 23, 2026Ziya Hosseynzadeh, Farjad Fallah, Farnoosh Fallah +3Multimodal Intent RecognitionPragmatic Reasoning in Language Models

    Speech and Language Processing Laboratory (SLPL), Sharif University of Technology, Tehran, Iran · School of Electrical and Computer Engineering, University of Tehran, Iran · Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

  2. Task-Preserving Multilingual Instruction Tuning for Open-Ended QA in WMT 2026 MIST

    Oct 21, 2026Aleix Sant, Jordi Luque, Carlos EscolanoMultilingual QAFine-Tuning Language Models

    Scientific Research, Telefónica Innovación Digital · Universitat Politècnica de Catalunya

  3. Understanding Data Augmentation under Label Noise

    Oct 20, 2026Hong Zheng, Fei TengData AugmentationLearning with Noisy Labels

    School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu, 611756, China · School of Computing and Artificial Intelligence and Engineering Research Center of Sustainable Urban Intelligent Transportation Southwest Jiaotong University and Ministry of Education Chengdu, 611756, China

  4. What Not to Erase: Saliency-Constrained Augmentation for Low-Resource Handwritten Character Recognition

    Oct 19, 2026Paraskevi Platanou, John PavlopoulosData AugmentationOptical Character Recognition

    Athens University of Economics and Business Archimedes, Athena Research Center, Greece

  5. Local data augmentation for imbalanced binary classification

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

    SAMM · LPP,MODAL

  6. 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

  7. 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

  8. ShadowCast: On-the-Fly Context-Aware Shadow Augmentation for Remote Sensing Image Segmentation

    Oct 16, 2026Dongho Yoon, Yechan Kim, Jongmin Joo +2Remote Sensing Image SegmentationData Augmentation

    Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Jeonnam-Gwangju, 61005, Korea · AI Laboratory, LIG Defense&Aerospace, 333 Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13488, Korea

  9. 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

  10. RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning

    Oct 13, 2026Yunpeng Jiang, Jianshu Hu, Han Fang +3Data-Efficient Robot LearningRobot Imitation Learning

    Global College, Shanghai Jiao Tong University, Shanghai, China · Shanghai Institute of Physical Intelligence and Robotics (PAIR), Shanghai, China · Duke Kunshan University, Jiangsu, China

  11. Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation

    Oct 13, 2026Huiyi Ren, Ziming Fang, Jiaming Cai +2Semi-Supervised Medical Image SegmentationData Augmentation

  12. Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design

    Oct 9, 2026D. Hudson SmithRepresentation LearningDisentangled Representation Learning

    Clemson University

  13. 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

  14. Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images

    Oct 8, 2026Danil Davydov, Bader Rasheed, Dmitriy VatolinImage Forgery DetectionDeepfake Detection

    Innopolis University, Innopolis, Russia · Laboratory of Innovative Technologies for Processing Video Content, Innopolis University, Innopolis, Russia

  15. 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

  16. How Do Transformers Learn to Represent Symmetries?

    Oct 7, 2026Eduardo Santos-Escriche, Valerie Engelmayer, Ya-Wei Eileen Lin +1Invariant Representation LearningTransformer

    Technical University of Munich Munich Center for Machine Learning · Massachusetts Institute of Technology

  17. Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models

    Oct 6, 2026Jivan Waber, Vanessa Piccolo, Yatin Dandi +1Representation LearningData Augmentation

    Information, Learning and Physics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory École Polytechnique Fédérale de Lausanne (EPFL)

  18. Impact of Data Augmentation on Confidence Calibration in Melanoma Classification

    Oct 5, 2026Morgan May, Simon Caton, Pierpaolo DondioData AugmentationMedical Image Classification

    Technological University Dublin, Dublin, Ireland · Research Ireland Centre for Research Training in Machine Learning, ML-Labs, Dublin, Ireland · University College Dublin, Dublin, Ireland +1

  19. Robotizing Human Videos with Physically Consistent Interactions

    Oct 5, 2026Ching-Lam Cheng, Shengfeng He, Bin ZhuRobotic GraspingData Augmentation

    Singapore Management University

  20. Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing

    Oct 5, 2026Ashik E Rasul, Hyung-Jin YoonAerial RoboticsSynthetic Data Augmentation

    Department of Mechanical and Nuclear Engineering Tennessee Technological University Cookeville, USA

  21. Graph Data Augmentation via Contrastive Generator Inversion (DCBA\texttt{DCBA})

    Oct 5, 2026Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4Synthetic Data AugmentationData Augmentation

    Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics +1

  22. Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning

    Oct 4, 2026Arathi Nair M, J. Harshan, Anwitaman DattaClass-Imbalanced LearningData Augmentation

    Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, India · Department of Electrical Engineering, Indian Institute of Technology Delhi, India · College of Computing and Data Science, Nanyang Technological University, Singapore

  23. CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization

    Oct 4, 2026Yucheng Song, Jincan Wang, Haokang Ding +3Data AugmentationCross-Domain Generalization

    School of Computer Science and Engineering, Central South University, Changsha, China · School of Software Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China

  24. You May Be Running the Wrong Inception Crop

    Oct 2, 2026Jason Chuan-Chih ChouData AugmentationImage Classification

    Cohere Labs Community

  25. Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

    Oct 1, 2026Yu Song, Zhigang Hua, Yan Xie +5Graph Data AugmentationData Augmentation

    Michigan State University · Meta

  26. PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization

    Oct 1, 2026Ahmed Sharshar, Asif Hanif, Naveen Kumar Kummari +2Adversarial TrainingData Augmentation

    Division of Computing and Mathematical Sciences Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

  27. Revisiting Risky Tackle Detection with Vision Transformers

    Sep 28, 2026Syed Ahsan Masud Zaidi, Lior Shamir, Scott DietrichData AugmentationSports Video Analysis

    Kansas State University, Manhattan, KS, USA · Albright College, Reading, PA, USA

  28. CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation

    Sep 28, 2026Md Shibly Sadique, Md Fayaz Bin Hossen, Michael L. Evans +4Class-Imbalanced LearningData Augmentation

    Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA · Interdisciplinary Schools, Old Dominion University, Norfolk, VA 23529, USA

  29. DualGuard: Dual-Mode Quality Control for Logic-Preserving Data Augmentation

    Sep 26, 2026Shenghao Li, Lin ZhaoData AugmentationSynthetic Data Augmentation

    Guangzhou University, Guangzhou, China

  30. ScreenHaystack: Finding Blind Zones in GUI Grounding

    Sep 25, 2026Chenyue Li, Xiaoxiao Sun, Yubo Deng +3GUI GroundingData Augmentation

    Stanford University