通过几何归一化提升预测模型在旋转翻转下的可靠性
CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization
- 利用几何姿态信息对数据进行归一化处理
- 在旋转、翻转等变化下仍保持准确的置信区间
- 适合需要鲁棒性保证的黑箱模型应用
我们研究了在几何数据偏移（如旋转、翻转）下的合取预测（CP）问题。尽管CP能为预测模型提供事后不确定性量化和形式化覆盖保证，但在导致模型性能下降的分布偏移下其有效性会失效。为此，我们提出将几何信息（如几何姿态）融入合取过程，以恢复其保证并确保在几何偏移下的鲁棒性。具体地，我们探索了姿态归一化技术作为此类信息提取的有效手段。在离散与连续偏移下，对比等变模型与基于增强的基线方法，结果表明将几何信息与CP结合，是一种在不依赖模型结构的前提下应对几何偏移的系统性方法，并适用于各类黑箱预测器。
原文摘要 · Abstract (English)
We study the problem of conformal prediction (CP) under geometric data shifts, where data samples are susceptible to transformations such as rotations or flips. While CP endows prediction models with post-hoc uncertainty quantification and formal coverage guarantees, their practicality breaks under distribution shifts that deteriorate model performance. To address this issue, we propose integrating geometric information--such as geometric pose--into the conformal procedure to reinstate its guarantees and ensure robustness under geometric shifts. In particular, we explore recent advancements on pose canonicalization as a suitable information extractor for this purpose. Evaluating the combined approach across discrete and continuous shifts and against equivariant and augmentation-based baselines, we find that integrating geometric information with CP yields a principled way to address geometric shifts while maintaining broad applicability to black-box predictors.
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