← 研踪 PaperTrailarXiv:2605.05136 · v4
Common Principal Component Networks for Domain Generalization
首次提交 2026-05-07 01:09
版本更新 2026-11-11 12:03
雷达观测 2026-11-13 11:11
摘要
Domain Generalization (DG) aims to learn representations that generalize to unseen target domains under out-of-distribution (OOD) shifts. Despite recent advances in invariant representation learning, explicitly capturing shared structures across source-domain distributions remains relatively underexplored. In this work, we integrate Common Principal Component Analysis (CPCA) into deep learning to identify a common orthogonal basis across multiple domains, proposing two complementary end-to-end differentiable frameworks. First, we introduce Common Principal Component Network (CPCNet), which unrolls the iterative Flury-Gautschi (FG) algorithm into fully differentiable neural layers, enabling covariance-based joint diagonalization within end-to-end representation learning. Second, to address rank deficiency arising from mini-batch covariance estimation in small-sample regimes, we develop Projection Pursuit-based Common Principal Component Network (PP-CPCNet), which directly optimizes a common orthogonal basis without explicit covariance estimation. Experiments validate the joint diagonalization capabilities of both methods, while evaluations on four standard DG benchmarks demonstrate competitive zero-shot generalization performance with favorable computational efficiency.
作者备注：This version consolidates our previous works, merging the contributions of the earlier paper available at arXiv:2607.22117
观测到的版本
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v4 · 2026-11-11 12:03 · 最新观测版本
Common Principal Component Networks for Domain Generalization
Domain Generalization (DG) aims to learn representations that generalize to unseen target domains under out-of-distribution (OOD) shifts. Despite recent advances in invariant representation learning, explicitly capturing shared structures across source-domain distributions remains relatively underexplored. In this work, we integrate Common Principal Component Analysis (CPCA) into deep learning to identify a common orthogonal basis across multiple domains, proposing two complementary end-to-end differentiable frameworks. First, we introduce Common Principal Component Network (CPCNet), which unrolls the iterative Flury-Gautschi (FG) algorithm into fully differentiable neural layers, enabling covariance-based joint diagonalization within end-to-end representation learning. Second, to address rank deficiency arising from mini-batch covariance estimation in small-sample regimes, we develop Projection Pursuit-based Common Principal Component Network (PP-CPCNet), which directly optimizes a common orthogonal basis without explicit covariance estimation. Experiments validate the joint diagonalization capabilities of both methods, while evaluations on four standard DG benchmarks demonstrate competitive zero-shot generalization performance with favorable computational efficiency.
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