提出自适应最优传输，实现分布间动态质量转移。
Partial Distribution Alignment via Adaptive Optimal Transport
- 基于数据内在结构自适应调整质量传递量
- 在域适应任务中显著优于现有方法
- 适合处理噪声、异常值和分布偏移场景
为解决经典最优传输中全质量或固定质量约束的缺陷，我们提出自适应最优传输，其核心在于具备自适应保质能力。该方法旨在回答如何在概率分布间自适应地传输概率质量这一基础性问题，是人工智能多个领域的重要课题。自适应最优传输能根据问题内在结构动态调整质量传递，理论分析揭示了其质量传输机制。进一步将该方法应用于机器学习中的分布对齐，实现源域与目标域的部分且自适应对齐，有效应对数据中普遍存在的噪声、异常值和分布偏移。在域适应基准测试中，实验结果表明该方法显著优于当前最先进的算法。
原文摘要 · Abstract (English)
To remedy the drawbacks of full-mass or fixed-mass constraints in classical optimal transport, we propose adaptive optimal transport which is distinctive from the classical optimal transport in its ability of adaptive-mass preserving. It aims to answer the mathematical problem of how to transport the probability mass adaptively between probability distributions, which is a fundamental topic in various areas of artificial intelligence. Adaptive optimal transport is able to transfer mass adaptively in the light of the intrinsic structure of the problem itself. The theoretical results shed light on the adaptive mechanism of mass transportation. Furthermore, we instantiate the adaptive optimal transport in machine learning application to align source and target distributions partially and adaptively by respecting the ubiquity of noises, outliers, and distribution shifts in the data. The experiment results on the domain adaptation benchmarks show that the proposed method significantly outperforms the state-of-the-art algorithms.
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