动态调节正则化，让模型更智能地适应卫星图像。
DARN: Dynamic Adaptive Regularization Networks for Efficient and Robust Foundation Model Adaptation
- 根据图像难易度动态调整丢弃率和通道激活，实现自适应正则化。
- 全量微调下在GeoBench上达86.66% mIoU，领先前人5.56个百分点。
- 适合需要强鲁棒性和小样本性能的地理空间分析场景。
基础模型（FMs）为地理空间分析提供了强大表征，但有效适配仍具挑战。标准方法如全量微调或高效冻结骨干网络，通常采用固定正则化策略，无法应对卫星影像显著异质性。本文提出动态自适应正则化网络（DARN），集成三项创新：(1) 轻量级任务复杂度预测器（TCP），估算每样本难度；(2) 自适应丢弃调制（ADM），动态调整丢弃率（0.1~0.5）；(3) 动态容量门控（DCG），调节通道激活。理论证明其优化可收敛至平稳点，并具备自适应信息瓶颈机制。实证显示，全量微调下，DARN在多任务GeoBench基准上达86.66% mIoU，较前人最优提升5.56个百分点；冻结骨干时，在Sen1Floods11上达90.5% mIoU，同时在AI4SmallFarms上提升9.5个百分点的分布外泛化能力，腐蚀误差降低17%，少数类表现更优。DARN为关键地理空间应用提供更智能、稳健、高效的基模适配方案。
原文摘要 · Abstract (English)
Foundation models (FMs) offer powerful representations for geospatial analysis, but adapting them effectively remains challenging. Standard adaptation methods, whether full fine-tuning or efficient frozen-backbone approaches, typically employ decoders with fixed regularization strategies, failing to account for the significant heterogeneity in satellite imagery. We introduce Dynamic Adaptive Regularization Networks (DARN), a novel decoder architecture designed to address this limitation. DARN integrates three key innovations: (1) a lightweight Task Complexity Predictor (TCP) that estimates per-sample difficulty, (2) Adaptive Dropout Modulation (ADM), dynamically adjusting dropout rates (from 0.1 to 0.5) based on predicted complexity, and (3) Dynamic Capacity Gating (DCG) that modulates channel activation. We provide theoretical justifications linking DARN's optimization to stationary point convergence and its mechanism to adaptive information bottlenecks. Empirically, DARN demonstrates exceptional performance across both major adaptation paradigms. In full fine-tuning (unfrozen backbone), DARN achieves a new state-of-the-art on the multi-task GeoBench benchmark (86.66% mIoU, +5.56 pp over prior SOTA). In efficient adaptation (frozen backbone), DARN achieves SOTA-competitive accuracy (90.5% mIoU on Sen1Floods11) while delivering substantial advantages crucial for real-world deployment: superior out-of-distribution (OOD) generalization (+9.5 pp mIoU on AI4SmallFarms), enhanced robustness (17% relative reduction in corruption error), and improved performance on minority classes. DARN offers a more intelligent, robust, and efficient approach to leveraging FMs in critical geospatial applications.
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