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Synthetic Data357 papers Synthetic Data233 papers Synthetic Data Augmentation155 papers Synthetic Dataset54 papers Synthetic Tabular Data42 papers Synthetic Training Data33 papers

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Papers

Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation
Jun 5, 2026Patrick Kage, Trevor Hedges, N. Siddharth +1Synthetic Data Augmentation Synthetic Samples
JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
Dec 6, 2025Shyama Sastha Krishnamoorthy Srinivasan, Harsh Pala, Mohan Kumar +1Healthcare Care
Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading
Jun 6, 2026Arati Uday KamatDecentralized Finance Rejection
OpenRFM: Dissecting Relational In-Context Learning
Jun 3, 2026Zhikai Chen, Junyu Yin, Jialiang Gu +5Relational Deep Learning In-Context Learning
Safeguarding Text-to-Image Generative Models Against Unauthorized Knowledge Distillation
May 21, 2026Yilan Gao, Sida Huang, Hongyuan Zhang +1Adversarial Examples Text-To-Image
Multimodal Distribution Matching for Vision-Language Dataset Distillation
May 22, 2026Jongoh Jeong, Hoyong Kwon, Minseok Kim +1Cross-Modal Distillation Image-Text Pairs
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
Apr 22, 2026Houman SafaaiGaussian Copula Maximum Likelihood
SED:Lightweight Saliency prediction for Event-based data via Distillation
Jun 12, 2026Romaric Mazna, Jean Martinet, Michele MagnoSaliency Convolutional
HAPI-EP: Towards Hybrid, Adaptive, and Predictive Digital Twins of Cardiac Electrophysiology
Jun 14, 2026Sumeet Vadhavkar, Xiajun Jiang, Yubo Ye +2Digital Twins Hybrid
Distribution-free inference on the number of changepoints
Sep 8, 2026Rohan Hore, Aaditya RamdasChange-Point Detection Finite-Sample
Online Conformal Prediction for Non-Exchangeable Panel Data
May 18, 2026Daohong Tu, Kay GieseckeConformal Prediction Conformal Calibration
SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
Sep 8, 2026Soroush Mehraban, Xin Lei Lin, Vida Adeli +5Single-Subject Gait Dataset Synthetic Motion Datasets
Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains
Jul 14, 2026Rwik Rana, Jesse Quattrociocchi, Christian Ellis +3Vehicle Dynamics Model Predictive Control
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
May 20, 2026Ziang Song, Ying Jin, Emmanuel J. CandèsFalse Discovery Rate Conformal Inference
Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift
May 9, 2026Bong Gyun Shin, Chan Sik Lee, Hyesun SuhDistribution Shifts Visibility
Group-invariant Coresets for Data-efficient Active Learning
Jul 1, 2026L. C. Ayres, J. C. M. Bermudez, S. J. M. de Almeida +1Geometry-Aware Uncertainty Coresets Active Learning
Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
Apr 30, 2026Akhil Gupta, Erhan GuvenExtended Kalman Filter Particle Filters
S $^3$ LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
May 29, 2026Chao Yin, Youran Dong, Shiqian Ma +2Bilevel Optimization Hyperparameter Optimization
Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs
Aug 31, 2026Jonathan Zheng, Zirui Shao, Alan Ritter +1Parallel
Multi-Distribution Robust Conformal Prediction
Jan 6, 2026Yuqi Yang, Ying JinConformal Prediction Finite-Sample Coverage Guarantees
Reward Learning from Best-of- $N$ Preference Data: Targets, Tradeoffs, and Design Principles
May 28, 2026Rattana Pukdee, Maria-Florina Balcan, Pradeep RavikumarTrade-Off Representation Space
Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations
Jul 7, 2026Juan Manuel Garcia-Perez, Carlos MateoGenerative Adversarial Network Layouts
Implicit Neural Representations of Individual Behavior
Jun 10, 2026Andrew Kang, Priya NarasimhanReinforcement-Learning Policies Implicit Neural Representations
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
May 21, 2026Stanislav R. Kirpichenko, Andrei V. Konstantinov, Lev V. UtkinSurvival Analysis Time-To-Event Data
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
Apr 23, 2026Luis Barba, Johannes Kirschner, Benjamin Bejar3D Latent Diffusion Model Cone-Beam Computed Tomography
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset
May 15, 2025Chengsen Wang, Qi Qi, Zhongwen Rao +2Zero-Shot Forecasting Multimodal Foundation Model
Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding
May 11, 2026Jianzhu Bao, Haozhen Zhang, Kuicai Dong +5Counterfactual Learning Chart
SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking
Sep 9, 2026Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis +1Text-Attributed Graph Graph Neural Networks
Learning Length-Extrapolatable Recurrent Models
Sep 8, 2026Hanwen JiangRecurrent Model Catastrophic Forgetting
Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security
Sep 7, 2026Joshua Salako, Folajimi Osikomaiya, Olakorede OlamijuPower Systems Ac Optimal Power Flow

Jun 5, 2026 · cs.CVJ/K move · Enter open · S save

Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation

Patrick Kage, Trevor Hedges, N. Siddharth, Pavlos Andreadis

School of Informatics The University of Edinburgh Edinburgh, Scotland EH8 9AB UK · Massachusetts Institute of Technology Lincoln Laboratory Lexington, MA 02421 USA

Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable for processing datasets. Among these approaches, contrastive learning provides a convenient mechanism for extracting structural representations from unannotated datasets. For natural imagery, the general approach is to use a variety of data-space augmentation methods in order to generate synthetic samples; however, for scientific observations data-space perturbations can fundamentally alter the underlying data. Our proposed method is to generate contrastive samples by perturbing the network weights rather than the underlying data, thus more closely preserving the structure of the data. We demonstrate this technique using a SimCLR-based pipeline applied over radar observations of meteors, and show performance gains under matched protocols.

Synthetic Data Augmentation Synthetic Samples Augmentation Contrastive Learning Generative Adversarial Network Radar

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