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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Improving Diversity in Black-box Few-shot Knowledge Distillation
Apr 28, 2026Tri-Nhan Vo, Dang Nguyen, Kien Do +1Synthetic Data Augmentation Few-Shot Learning
Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia
Diverse Image Priors for Black-box Data-free Knowledge Distillation
Apr 28, 2026Tri-Nhan Vo, Dang Nguyen, Trung Le +2Synthetic Data Augmentation Data-Free Knowledge Distillation
Applied Artificial Intelligence Initiative, Deakin University, Geelong, Australia · Department of Data Science & AI, Monash University, Melbourne, Australia
EVT-Based Generative AI for Tail-Aware Channel Estimation
Apr 27, 2026Parmida Valiahdi, Niloofar Mehrnia, Walid Saad +1Synthetic Data Augmentation Wireless Communications
∗Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey · †Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · ‡Department of Electrical and Computer Engineering, Virginia Tech, Virginia, United States
Geometry-Conditioned Diffusion for Occlusion-Robust In-Bed Pose Estimation
Apr 26, 2026Navid Aslankhani Khameneh, Marco Carletti, Cigdem BeyanSynthetic Data Augmentation Latent Diffusion Models
Department of Computer Science, University of Verona, Verona, Italy · 2EVS - Embedded Vision Systems Srl, Verona, Italy
Generating Synthetic Malware Samples Using Generative AI
Apr 23, 2026Tiffany Bao, Kylie Trousil, Quang Duy Tran +2Class-Imbalanced Learning Synthetic Data Augmentation
Department of Computer Science, Boston University, Boston, MA 02215, USA · Department of Computer Science, University of Wisconsin–La Crosse, La Crosse, WI 54601, USA · Department of Computer Science, San José State University, San Jose, CA 95192, USA +2
Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation
Apr 23, 2026Yuanchen Fei, Yude Zou, Zejian Kang +3Synthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Hunan University · Shanghai Jiaotong University · Westlake University +3
A Probabilistic Framework for Improving Dense Object Detection in Underwater Image Data via Annealing-Based Data Augmentation
Apr 23, 2026Eleanor Wiesler, Trace BaxleySynthetic Data Augmentation Data Augmentation
Harvard Department of Mathematics. · Harvard Department of Computer Science.
Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Apr 22, 2026Karim Aly, Alexei Sharpanskykh, Jacco HoekstraSynthetic Data Augmentation Synthetic Data Generation
Control and Operations, Faculty of Aerospace Engineering, Delft University of Technology (TU Delft), Kluyverweg 1, Delft, 2629 HS, South Holland, Netherlands
Accelerating New Product Introduction for Visual Quality Inspection via Few-Shot Diffusion-Based Defect Synthesis
Apr 22, 2026Serkan Hamdi Güğül, Kemal Levi, Burak AcarSynthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Relimetrics, Inc.
IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
Apr 22, 2026Mingchun Sun, Rongqiang Zhao, Muhammad Abdul Munnaf +1Synthetic Data Augmentation Data Augmentation
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China · State Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China · Agricultural Biosystems Engineering, Wageningen University and Research, Wageningen 6703 CH, Netherlands
CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
Apr 21, 2026Nassim Sadallah, Mohand Saïd AlliliSynthetic Data Augmentation Image Generation
LARIVIA Lab, Department of Computer Science and Engineering · Gatineau, QC, Canada
AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
Apr 21, 2026Jiagao Hu, Daiguo Zhou, Danzhen Fu +6Synthetic Data Augmentation Long-Horizon Video Generation
MiLM Plus, Xiaomi Inc. · Wuhan, China · Xiaomi Inc. +1
Model in Distress: Sentiment Analysis on French Synthetic Social Media
Apr 20, 2026Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois +3Synthetic Data Augmentation Synthetic Data Generation
PleIAs, Paris, France · CAIRO, THWS, Würzburg, Germany · EPFL, Lausanne, Switzerland +1
Class-specific diffusion models improve military object detection in a low-data domain
Apr 20, 2026Ella P. Fokkinga, Jan Erik van Woerden, Thijs A. Eker +4Synthetic Data Augmentation Diffusion Model Guidance
TNO - Intelligent Imaging, Oude Waalsdorperweg 63, the Hague, the Netherlands
LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
Apr 20, 2026Pranshav Gajjar, Manan Tiwari, Sayanta Seth +1Distribution Shift Robustness Synthetic Data Augmentation
NextG Wireless Lab, North Carolina State University, NC
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
Apr 18, 2026Chongsheng Zhang, Hao Wang, Zelong Yu +7Class-Imbalanced Learning Synthetic Data Augmentation
Henan University, China · Department of Statistics, LMU Munich · MCML Munich +1
AeroDeshadow: Physics-Guided Shadow Synthesis and Penumbra-Aware Deshadowing for Aerospace Imagery
Apr 17, 2026Wei Lu, Zi-Yang Bo, Fei-Fei Sang +3Remote Sensing Image Understanding Synthetic Data Augmentation
MOE Key Laboratory of ICSP, IMIS Laboratory of Anhui, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Zenmorn-AHU AI Joint Laboratory, School of Computer Science and Technology, Anhui University, Hefei 230601, China · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China
AHS: Adaptive Head Synthesis via Synthetic Data Augmentations
Apr 17, 2026Taewoong Kang, Hyojin Jang, Sohyun Jeong +4Synthetic Data Augmentation Unpaired Image-to-Image Translation
KAIST · Korea University · FLIPTION
Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation
Apr 16, 2026Hassan Ali, Doreen Jirak, Luca Müller +1Synthetic Data Augmentation Image-to-Video Generation
Knowledge Technology Group, Department of Informatics, University of Hamburg, Germany · Behavioral Lab, Department of Product Development, University of Antwerp, Belgium
Generative Data Augmentation for Skeleton Action Recognition
Apr 16, 2026Xu Dong, Wanqing Li, Anthony Adeyemi-Ejeye +1Skeleton-Based Action Recognition Synthetic Data Augmentation
Innovative Media Lab, University of Surrey, Guildford, UK · Advanced Multimedia Research Lab, University of Wollongong, Wollongong, Australia
Data Synthesis Improves 3D Myotube Instance Segmentation
Apr 16, 2026David Exler, Nils Friederich, Martin Krüger +5Synthetic Data Augmentation Image Segmentation
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. · Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. · CeMOS Research and Transfer Center, Technische Hochschule Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany.
Improving Machine Learning Performance with Synthetic Augmentation
Apr 16, 2026Mel Sohm, Charles Dezons, Sami Sellami +2Synthetic Data Augmentation Quantitative Finance
University of California, Berkeley
SynMulti: Synthetic-to-Real Learning for Multimodal Video Understanding
Apr 14, 2026Tanzila Rahman, Renjie Liao, Leonid SigalSynthetic-to-Real Domain Adaptation Synthetic Data Augmentation
University of British Columbia · Vector Institute for AI · Canada CIFAR AI Chair
VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images
Apr 10, 2026Guanyu Zhou, Yida Yin, Wenhao Chai +3Visual Question Answering Synthetic Data Augmentation
Princeton University · New York University
ML-based approach to classification and generation of structured light propagation in turbulent media
Apr 4, 2026Aokun Wang, Anjali Nair, Zhongjian Wang +1Synthetic Data Augmentation Conditional Diffusion Models
Division of Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371 Singapore · Committee on Computational and Applied Mathematics, University of Chicago, Chicago, IL 60637 USA · Departments of Statistics and Mathematics, University of Chicago, Chicago, IL 60637 USA
Overcoming the Modality Gap in Context-Aided Forecasting
Mar 12, 2026Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4Synthetic Data Augmentation Multimodal Pretraining
ServiceNow AI Research, Montréal, Canada · Department of Civil Engineering, McGill University, Montréal, Canada · DIRO, Université de Montréal, Montréal, Canada +1
Bootstrapping Audiovisual Speech Recognition in Zero-AV-Resource Scenarios
Mar 9, 2026Pol Buitrago, Javier HernandoSynthetic Data Augmentation Audio-Visual Representation Learning
Barcelona Supercomputing Center (BSC), Spain · Universitat Polit`ecnica de Catalunya (UPC), Spain
Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
Mar 3, 2026Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen +1Synthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations
Feb 23, 2026Blaž Rolih, Matic Fučka, Filip Wolf +1Synthetic Data Augmentation Remote Sensing Change Detection
University of Ljubljana, Faculty of Computer and Information Science, Slovenia
MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling
Feb 19, 2026Payel Bhattacharjee, Osvaldo Simeone, Ravi TandonReward Modeling Synthetic Data Augmentation
University of Arizona · Tucson, AZ, USA · Northeastern University London +1

Apr 28, 2026, cs.CVJ/K move · Enter open · S save

Improving Diversity in Black-box Few-shot Knowledge Distillation

Tri-Nhan Vo, Dang Nguyen, Kien Do, Sunil Gupta

Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia

Synthetic Data Augmentation Few-Shot Learning Knowledge Distillation Generative Adversarial Networks

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Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teacher) to a smaller network (student) with little sacrifice in performance. However, most KD methods require a large training set and internal access to the teacher, which are rarely available due to various restrictions. These challenges have originated a more practical setting known as black-box few-shot KD, where the student is trained with few images and a black-box teacher. Recent approaches typically generate additional synthetic images but lack an active strategy to promote their diversity, a crucial factor for student learning. To address these problems, we propose a novel training scheme for generative adversarial networks, where we adaptively select high-confidence images under the teacher's supervision and introduce them to the adversarial learning on-the-fly. Our approach helps expand and improve the diversity of the distillation set, significantly boosting student accuracy. Through extensive experiments, we achieve state-of-the-art results among other few-shot KD methods on seven image datasets. The code is available at https://github.com/votrinhan88/divbfkd.

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