Papers Synthetic Data Generation
“Synthetic Data Generation” 태그가 달린 논문 1,272편 · 필터 해제
Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes
Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Prior work addresses it either through pixe…
Synthetic Data GenerationSynThermFace: Amplifying Limited Paired Data for Visible-Thermal Face Recognition via Synthetic Data Generation
Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when high-quality RGB images cannot be captured. Cross-spectral face reco…
Synthetic Data GenerationFace RecognitionFrom Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning
Financial question answering (QA) has emerged as a key benchmark for evaluating the performance of Large Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich tex…
Synthetic Data GenerationSemantic SimilarityQuestion AnsweringAnswer GenerationConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models
Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often concentrated in loca…
Synthetic Data GenerationActive LearningNVIDIA Cosmos-H-Dreams: Real-Time Generative Physics Simulation for Surgical Robotics
Generative simulation for surgical robotics still lacks real-time interaction. Physical-robot experiments, often involving animal or cadaver labs, are time-consuming, costly, and difficult to reproduce, while classical s…
Synthetic Data GenerationNovel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-qua…
Synthetic Data GenerationGraph Neural NetworkKnowledge GraphsLimitations of Synthetic Data Generation in Specialized Data-Scarce Domains
Advances in diffusion-based generative models have motivated the use of synthetic image generation to alleviate data scarcity in vision tasks. While this strategy has shown promise in natural image benchmarks such as Ima…
Synthetic Data GenerationData AugmentationImage GenerationGENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. …
Synthetic Data GenerationiARCS: Iterative Agentic RL for Controllable 3D Scene Generation
Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional cons…
Synthetic Data GenerationReinforcement LearningScene GenerationPatient Pose Assessment Using a CT-Based Framework for Synthetic Data Generation
An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one of the most important factors determining the diagnostic quality. Sin…
Synthetic Data GenerationVSMP-IMU: Video-Grounded Semantic Motion Programs for Sensor-Aware Synthetic IMU Generation
Wearable human activity recognition (HAR) is often limited by the scarcity of labeled sensor data, especially in low-resource, class-imbalanced, and subject-generalization settings. Synthetic IMU generation can reduce th…
Human Activity RecognitionSynthetic Data GenerationSKT: Skill-Use Training at Scale via Verified Synthetic Data Generation
Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that current models can effectively identify, app…
Synthetic Data GenerationSPARC Segmentation to Prediction via Affine Regression and Counterfactuals
Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implic…
Synthetic Data GenerationSynthetic data generation framework for quality control automation in gravure printing
Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in…
Synthetic Data GenerationAutomated Synthesis and Adversarial Validation of Executable Causal Research Pipelines
While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes successfully yet relies on invalid causal assumptions. We present th…
Synthetic Data GenerationPersian Pixel: A large-scale synthetic OCR dataset for Persian language
Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spoken by more than 110 million people across multiple countries. This gap arises fr…
Synthetic Data GenerationEnvironment-free Synthetic Data Generation for API-Calling Agents
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs a…
Synthetic Data GenerationMetric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data generation through 3D modelling and rendering …
Synthetic Data GenerationSmall Object DetectionSYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorithms applied to Electronic Health Records show promise for accelerating…
Synthetic Data GenerationQuantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning
Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias. We present a hybrid quantum-classical framework for synthetic data generati…
Synthetic Data GenerationDimensionality ReductionData Augmentation