synthetic data generation
Synthetic data generation refers to the creation of artificial data points based on certain statistical properties or simulations. This technique is often utilized when real data is scarce or to augment datasets in training machine learning models.
- A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
- AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition
- CORAL: Disentangling Latent Representations in Long-Tailed Diffusion
- ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions
- DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding
- Diffusion Federated Dataset
- Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning
- Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection
- PANGEA: Projection-Based Augmentation with Non-Relevant General Data for Enhanced Domain Adaptation in LLMs
- Private Evolution Converges
- Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction
- ROSE: Remove Objects with Side Effects in Videos
- Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
- SimpleStrat: Diversifying Language Model Generation with Stratification
- Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
- V2V: Scaling Event-Based Vision through Efficient Video-to-Voxel Simulation