提出无协方差矩阵的域泛化方法，提升小样本下的鲁棒性。
Projection Pursuit CPCANet for Domain Generalization
- 在Stiefel流形上学习全局正交基，避免小批量下协方差矩阵秩不足问题
- 在四个基准测试中达到最新性能（SOTA），训练过程稳定
- 适合需要高鲁棒性的跨域场景，如医疗图像分析
域泛化（DG）旨在学习对分布偏移具有鲁棒性的表征。近年来的几何对齐方法，如CPCANet，通过批内公共主成分分析（CPCA）提取域不变结构。然而，由于小批量训练中的小样本问题，CPCANet存在协方差估计秩不足的缺陷。为此，我们提出投影追踪型CPCANet（PP-CPCANet），一种无需协方差矩阵的框架，通过Cayley变换联合优化网络参数与在Stiefel流形上的全局正交基。进一步引入对称性破坏的分离中位数投影分散目标，以获得密集且稳健的优化信号。在四个域泛化基准测试上，PP-CPCANet实现了最新性能（SOTA），同时保持训练稳定性。
原文摘要 · Abstract (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.
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