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 27 篇，第 1 / 1 页

1. NeurIPS 2025Achilles' Heel of Mamba: Essential difficulties of the Mamba architecture demonstrated by synthetic data
2. NeurIPS 2025Amortized Active Generation of Pareto Sets
3. NeurIPS 2025AR-RAG: Autoregressive Retrieval Augmentation for Image Generation
4. NeurIPS 2025AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition
5. NeurIPS 2025ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions
6. NeurIPS 2025Constrained Posterior Sampling: Time Series Generation with Hard Constraints
7. NeurIPS 2025Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
8. NeurIPS 2025FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies
9. NeurIPS 2025Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
10. NeurIPS 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
11. NeurIPS 2025Increasing the Utility of Synthetic Images through Chamfer Guidance
12. NeurIPS 2025KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
13. NeurIPS 2025MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial Reasoning
14. NeurIPS 2025MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning
15. NeurIPS 2025MoleBridge: Synthetic Space Projecting with Discrete Markov Bridges
16. NeurIPS 2025Non-Adaptive Adversarial Face Generation
17. NeurIPS 2025PANGEA: Projection-Based Augmentation with Non-Relevant General Data for Enhanced Domain Adaptation in LLMs
18. NeurIPS 2025Parameterized Synthetic Text Generation with SimpleStories
19. NeurIPS 2025Private Evolution Converges
20. NeurIPS 2025ROSE: Remove Objects with Side Effects in Videos
21. NeurIPS 2025Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation
22. NeurIPS 2025Self-Adapting Language Models
23. NeurIPS 2025SimpleStrat: Diversifying Language Model Generation with Stratification
24. NeurIPS 2025Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping
25. NeurIPS 2025Synthetic Series-Symbol Data Generation for Time Series Foundation Models
26. NeurIPS 2025Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations
27. NeurIPS 2025Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions

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。