用学习到的自然噪声模型提升模型抗干扰能力，效果优于传统方法。
From Snow to Rain: Evaluating Robustness, Calibration, and Complexity of Model-Based Robust Training
- 基于学习的噪声模型生成真实干扰，结合对抗训练增强鲁棒性。
- 在雪雨干扰下，新方法准确率显著高于基线，计算开销更低。
- 适合安全敏感场景，如自动驾驶中的交通标志识别。
深度学习在安全关键领域面临自然干扰下的鲁棒性挑战。本文研究一类基于模型的训练方法，利用学习到的干扰变异模型生成真实感干扰，并提出混合策略：在扰动空间中结合随机覆盖与对抗优化。在包含雪、雨干扰的CURE-TSR数据集上评估了不同干扰强度下的准确率、校准性和训练复杂度。结果表明，基于模型的方法持续优于基础模型、对抗训练和AugMix等基线；其中，基于模型的对抗训练在所有干扰下表现最优，但计算成本更高；而基于模型的数据增强在仅需$T$倍计算量的情况下达到相当的鲁棒性，性能无统计显著下降。研究强调了学习自然变异模型对捕捉真实多样性的重要性，为构建更稳健、更校准的模型提供了可行路径。
原文摘要 · Abstract (English)
Robustness to natural corruptions remains a critical challenge for reliable deep learning, particularly in safety-sensitive domains. We study a family of model-based training approaches that leverage a learned nuisance variation model to generate realistic corruptions, as well as new hybrid strategies that combine random coverage with adversarial refinement in nuisance space. Using the Challenging Unreal and Real Environments for Traffic Sign Recognition dataset (CURE-TSR), with Snow and Rain corruptions, we evaluate accuracy, calibration, and training complexity across corruption severities. Our results show that model-based methods consistently outperform baselines Vanilla, Adversarial Training, and AugMix baselines, with model-based adversarial training providing the strongest robustness under across all corruptions but at the expense of higher computation and model-based data augmentation achieving comparable robustness with $T$ less computational complexity without incurring a statistically significant drop in performance. These findings highlight the importance of learned nuisance models for capturing natural variability, and suggest a promising path toward more resilient and calibrated models under challenging conditions.
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