合成数据生成论文趋势 2023–2026

主要结论

• 合成数据生成论文从 2023 年的 72 篇增长到 2026 年的 96 篇，三年间扩大到约 1.3 倍。
• NeurIPS 是发合成数据生成论文最多的会议，2023–2026 年共 114 篇，占四个会议总量的 41%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 6 篇增至 2026 年的 20 篇。
• 在「以数据为中心 AI」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的弱监督是 1.3 倍，合成数据生成是 1.3 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026Breaking the Quality–Privacy Tradeoff in Tabular Data Generation via In-Context Learning
• ICML 2026Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
• ICLR 2026Critic–Adviser–Reviser Cyclic Refinement: Towards High-Quality EMR Corpus Generation with LLMs
• CVPR 2026Beyond Objects Contextual Synthetic Data Generation for Fine-Grained Classification
• NeurIPS 2025Achilles' Heel of Mamba: Essential difficulties of the Mamba architecture demonstrated by synthetic data
• ICML 2025Synthetic Text Generation for Training Large Language Models via Gradient Matching
• ICLR 2025Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation
• CVPR 2025Synthetic Visual Genome
• NeurIPS 2024DECO-Bench: Unified Benchmark for Decoupled Task-Agnostic Synthetic Data Release
• ICML 2024CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
• ICLR 2024A Linear Algebraic Framework for Counterfactual Generation
• CVPR 2024Learning from Synthetic Human Group Activities
• NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
• ICML 2023Data Feedback Loops: Model-driven Amplification of Dataset Biases
• ICLR 2023Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation
• CVPR 2023Fake It Till You Make It: Learning Transferable Representations From Synthetic ImageNet Clones
• NeurIPS 2026Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
• ICML 2026Counterfactual Residual Data Augmentation for Regression
• ICLR 2026Making, Not Taking, the Best of N
• CVPR 2026Order Matters 3D Shape Generation from Sequential VR Sketches

全部合成数据生成论文

共 24 篇，第 1 / 1 页

1. NeurIPS 2026Breaking the Quality–Privacy Tradeoff in Tabular Data Generation via In-Context Learning
2. NeurIPS 2026DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation
3. NeurIPS 2026EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
4. NeurIPS 2026Firefly: Illuminating Verified Real-world Tool Call Data Generation
5. NeurIPS 2026HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching
6. NeurIPS 2026How Data Augmentation Shapes Neural Representations
7. NeurIPS 2026InvestigationWorlds: An Agentic Environment for Legal Investigation
8. NeurIPS 2026Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency
9. NeurIPS 2026L2P: Unlocking Latent Potential for Pixel Generation
10. NeurIPS 2026Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
11. NeurIPS 2026OopsWorld! Operation-Grounded Seamless World Generation
12. NeurIPS 2026Persona Generators: Generating Diverse Synthetic Personas for Arbitrary Contexts
13. NeurIPS 2026Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation
14. NeurIPS 2026Quest: Training Frontier Deep Research Agents with Fully Synthetic Tasks
15. NeurIPS 2026Risk-guided Estimation-aware Acceptance for Learning with Synthetic Data
16. NeurIPS 2026ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation
17. NeurIPS 2026SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data
18. NeurIPS 2026StereoPep: Do Molecular Models Understand Stereochemistry? A Benchmark on Synthetic Diastereomeric Peptides
19. NeurIPS 2026Synthon Contrastive Learning for Synthesizable 3D Molecule Generation
20. NeurIPS 2026TailDiff: Elicitable Tail-Guided Diffusion for Risk-Sensitive Generation
21. NeurIPS 2026Targeted Synthetic Control Method
22. NeurIPS 2026TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
23. NeurIPS 2026Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training
24. NeurIPS 2026Transformers Converge to Invariant Algorithmic Cores

常见问题

2023–2026 年四大会议一共有多少篇合成数据生成论文？

共 277 篇，按年份分别是 2023 年 72 篇、2024 年 41 篇、2025 年 68 篇、2026 年 96 篇。

NeurIPS 2026 有多少篇合成数据生成论文？

NeurIPS 2026 有 24 篇合成数据生成论文，上一届是 27 篇。

ICLR 2026 有多少篇合成数据生成论文？

ICLR 2026 有 26 篇合成数据生成论文，上一届是 18 篇。

ICML 2026 有多少篇合成数据生成论文？

ICML 2026 有 21 篇合成数据生成论文，上一届是 9 篇。

CVPR 2026 有多少篇合成数据生成论文？

CVPR 2026 有 25 篇合成数据生成论文，上一届是 14 篇。

哪个会议的合成数据生成论文最多？

NeurIPS，2023–2026 年共 114 篇。