面向域泛化的投影寻踪CPCANet

摘要

领域泛化（DG）旨在学习对分布偏移鲁棒的表示。近期几何对齐方法（如CPCANet）通过批量级公共主成分分析（CPCA）提取领域不变结构。然而，CPCANet因小批量训练中的小样本问题导致协方差估计秩不足。为解决此限制，我们提出投影追踪CPCANet（PP-CPCANet），这是一种无协方差框架，可在斯蒂弗尔流形上学习全局正交基，并通过凯莱变换将其与网络参数联合优化。我们进一步引入对称破缺的分离中位PP散度目标，以提取具有密集稳健优化信号的公共主成分（CPCs）。在四个DG基准上的实验表明，PP-CPCANet在保持稳定训练的同时实现了最先进的性能。

English

Domain Generalization (DG) aims to learn representations robust to distribution shifts. Recent geometric alignment methods, such as CPCANet, extract domain-invariant structures through batch-wise Common Principal Component Analysis (CPCA). However, CPCANet suffers from rank-deficient covariance estimation due to the small-sample-size issue in mini-batch training. To address this limitation, we propose Projection Pursuit CPCANet (PP-CPCANet), a covariance-free framework that learns a global orthogonal basis on the Stiefel manifold and jointly optimizes it with network parameters via the Cayley transform. We further introduce a symmetry-breaking detached-median PP dispersion objective to extract common principal components (CPCs) with dense and robust optimization signals. Experiments on four DG benchmarks show that PP-CPCANet achieves SOTA performance while maintaining stable training.