Synthetic Data Generation
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Benchmarks
UNSW-NB15
Most implemented
Wasserstein GAN
Improved Training of Wasserstein GANs
Generating Multidimensional Clusters With Support Lines
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Synthetic QA Corpora Generation with Roundtrip Consistency
Papers
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 Graphs