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 277 篇，第 2 / 6 页

51. ICLR 2026Using maximal information auxiliary variables to improve synthetic data generation based on TabPFN foundation models
52. ICML 2026$\texttt{FHAIM}$: Fully Homomorphic AIM for Private Tabular Synthetic Data Generation
53. ICML 2026Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
54. ICML 2026AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
55. ICML 2026Configurable Reward Model for Balanced Safety Alignment
56. ICML 2026Counterfactual Residual Data Augmentation for Regression
57. ICML 2026Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
58. ICML 2026Efficient Synthetic Network Generation via Latent Embedding Reconstruction
59. ICML 2026EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
60. ICML 2026FedPDG: Prediction Discrepancy–Guided Data Generation for Heterogeneous Federated Learning
61. ICML 2026Hierarchical Abstract Tree for Cross-Document Retrieval Augmented Generation
62. ICML 2026Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
63. ICML 2026Identifying Learnwares via Reduced Neural Conditional Mean Embedding
64. ICML 2026Is Generation Required for Data-Efficient Perception?
65. ICML 2026Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization
66. ICML 2026OCNR: Stabilizing Self-Play by Mitigating Iteration-Collapse With One-Class Novelty Rewards
67. ICML 2026Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
68. ICML 2026Rapid Poison: Practical Poisoning Attacks Against the Rapid Response Framework
69. ICML 2026Relational In-Context Learning via Synthetic Pre-training with Structural Prior
70. ICML 2026ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
71. ICML 2026SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
72. ICML 2026VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
73. NeurIPS 2026Breaking the Quality–Privacy Tradeoff in Tabular Data Generation via In-Context Learning
74. NeurIPS 2026DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation
75. NeurIPS 2026EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
76. NeurIPS 2026Firefly: Illuminating Verified Real-world Tool Call Data Generation
77. NeurIPS 2026HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching
78. NeurIPS 2026How Data Augmentation Shapes Neural Representations
79. NeurIPS 2026InvestigationWorlds: An Agentic Environment for Legal Investigation
80. NeurIPS 2026Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency
81. NeurIPS 2026L2P: Unlocking Latent Potential for Pixel Generation
82. NeurIPS 2026Learning with Synthetic Data via SGD in High-Dimensional Linear Regression
83. NeurIPS 2026OopsWorld! Operation-Grounded Seamless World Generation
84. NeurIPS 2026Persona Generators: Generating Diverse Synthetic Personas for Arbitrary Contexts
85. NeurIPS 2026Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation
86. NeurIPS 2026Quest: Training Frontier Deep Research Agents with Fully Synthetic Tasks
87. NeurIPS 2026Risk-guided Estimation-aware Acceptance for Learning with Synthetic Data
88. NeurIPS 2026ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation
89. NeurIPS 2026SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data
90. NeurIPS 2026StereoPep: Do Molecular Models Understand Stereochemistry? A Benchmark on Synthetic Diastereomeric Peptides
91. NeurIPS 2026Synthon Contrastive Learning for Synthesizable 3D Molecule Generation
92. NeurIPS 2026TailDiff: Elicitable Tail-Guided Diffusion for Risk-Sensitive Generation
93. NeurIPS 2026Targeted Synthetic Control Method
94. NeurIPS 2026TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
95. NeurIPS 2026Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training
96. NeurIPS 2026Transformers Converge to Invariant Algorithmic Cores
97. CVPR 2025AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data
98. CVPR 2025AKiRa: Augmentation Kit on Rays for Optical Video Generation
99. CVPR 2025Be More Specific: Evaluating Object-centric Realism in Synthetic Images
100. CVPR 2025Controllable Human Image Generation with Personalized Multi-Garments

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。