基础模型适配论文趋势 2023–2026

主要结论

• 基础模型适配论文从 2023 年的 159 篇增长到 2026 年的 602 篇，三年间扩大到约 3.8 倍。
• NeurIPS 是发基础模型适配论文最多的会议，2023–2026 年共 531 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是深度学习 · foundation models：同时属于两个主题的论文从 2023 年的 7 篇增至 2026 年的 66 篇。
• 在「元学习与迁移」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的持续学习是 1.7 倍，基础模型适配是 3.8 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 2026(Mis)generalization of Helpful-Only Fine-Tuning
• ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
• ICLR 2026Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
• CVPR 2026CD-Buffer Complementary Dual-Buffer Framework for Test-Time Adaptation in Adverse Weather
• NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
• ICML 2025A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
• ICLR 2025Better autoregressive regression with LLMs via regression-aware fine-tuning
• CVPR 20255%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
• NeurIPS 2024Instruction Tuning Large Language Models to Understand Electronic Health Records
• ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
• ICML 2023Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
• ICLR 2023A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
• CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
• NeurIPS 2026A Graph Foundation Model for Unified Clustering
• ICML 2026AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
• ICLR 2026Bridging Radiology and Pathology Foundation Models via Concept-Based Multimodal Co-Adaptation
• CVPR 2026Fed-ADE Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

全部基础模型适配论文

共 18 篇，第 1 / 1 页

1. ICLR 2023A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
2. ICLR 2023Consolidator: Mergable Adapter with Group Connections for Visual Adaptation
3. ICLR 2023Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
4. ICLR 2023Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
5. ICLR 2023Energy-Based Test Sample Adaptation for Domain Generalization
6. ICLR 2023ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
7. ICLR 2023Graph Domain Adaptation via Theory-Grounded Spectral Regularization
8. ICLR 2023Hyper-Decision Transformer for Efficient Online Policy Adaptation
9. ICLR 2023Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation
10. ICLR 2023Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
11. ICLR 2023Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
12. ICLR 2023Self-Distillation for Further Pre-training of Transformers
13. ICLR 2023Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
14. ICLR 2023The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
15. ICLR 2023Token Merging: Your ViT But Faster
16. ICLR 2023TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning
17. ICLR 2023Using Language to Extend to Unseen Domains
18. ICLR 2023When Source-Free Domain Adaptation Meets Learning with Noisy Labels

常见问题

2023–2026 年四大会议一共有多少篇基础模型适配论文？

共 1,492 篇，按年份分别是 2023 年 159 篇、2024 年 270 篇、2025 年 461 篇、2026 年 602 篇。

NeurIPS 2026 有多少篇基础模型适配论文？

NeurIPS 2026 有 182 篇基础模型适配论文，上一届是 174 篇。

ICLR 2026 有多少篇基础模型适配论文？

ICLR 2026 有 187 篇基础模型适配论文，上一届是 149 篇。

ICML 2026 有多少篇基础模型适配论文？

ICML 2026 有 142 篇基础模型适配论文，上一届是 76 篇。

CVPR 2026 有多少篇基础模型适配论文？

CVPR 2026 有 91 篇基础模型适配论文，上一届是 62 篇。

哪个会议的基础模型适配论文最多？

NeurIPS，2023–2026 年共 531 篇。