Technology Category
Machine Learning: Transfer, Domain Adaptation, Multi-Task LearningMultiagent Systems: Adversarial AgentsComputer Vision: Adversarial Attacks & Robustness
Decision Boundary Optimization-Informed Domain Adaptation
🤖 AI Summary
Existing MMD-based domain adaptation methods solely align source and target data distributions, neglecting optimization of the classifier’s decision boundary—thus failing to reduce the theoretical upper bound on classification error. To address this, we propose Decision Boundary-aware MMD (DB-MMD), the first method to incorporate classifier gradient information into the kernel mean embedding framework, enabling joint optimization of distribution alignment and decision boundary refinement. We rigorously derive its generalization error bound. Our approach introduces differentiable decision boundary regularization and gradient-coupled modeling, and is compatible with mainstream frameworks such as MEDA and DGA-DA. Evaluated on eight standard unsupervised domain adaptation benchmarks, DB-MMD achieves up to a 9.5 percentage-point improvement over conventional MMD baselines. It significantly enhances transfer robustness and provides stronger theoretical guarantees for domain adaptation.
Technology Category
Application Category
📝 Abstract
Maximum Mean Discrepancy (MMD) is widely used in a number of domain adaptation (DA) methods and shows its effectiveness in aligning data distributions across domains. However, in previous DA research, MMD-based DA methods focus mostly on distribution alignment, and ignore to optimize the decision boundary for classification-aware DA, thereby falling short in reducing the DA upper error bound. In this paper, we propose a strengthened MMD measurement, namely, Decision Boundary optimization-informed MMD (DB-MMD), which enables MMD to carefully take into account the decision boundaries, thereby simultaneously optimizing the distribution alignment and cross-domain classifier within a hybrid framework, and leading to a theoretical bound guided DA. We further seamlessly embed the proposed DB-MMD measurement into several popular DA methods, e.g., MEDA, DGA-DA, to demonstrate its effectiveness w.r.t different experimental settings. We carry out comprehensive experiments using 8 standard DA datasets. The experimental results show that the DB-MMD enforced DA methods improve their baseline models using plain vanilla MMD, with a margin that can be as high as 9.5.
Problem
Research questions and friction points this paper is trying to address.
Optimize decision boundary for classification-aware DA
Strengthen MMD measurement for distribution alignment
Embed DB-MMD into popular DA methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decision Boundary optimization-informed MMD
Simultaneously optimizes distribution alignment
Seamlessly embedded into popular DA methods
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