这篇论文解决了流式数据的方差缩减问题，ARROW算法能在线对齐MMD和CORAL，效果不输离线方法，搞领域自适应的人可以看看。
该论文提出ARROW，首个针对最大均值差异（MMD）和相关对齐（CORAL）损失函数的在线随机方差缩减（SVR）算法。ARROW维护对齐统计量的移动平均参考，并自适应重加权传入的minibatch以对齐minibatch与参考统计量。通过松弛重加权方案使权重优化问题可解。实验表明，ARROW在运行时间、方差缩减程度和目标域准确率上与离线算法竞争力相当。
Online Variance Reduction for Domain Adaptation on Streaming Data
This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions. Although various offline SVR algorithms for these losses have been proposed, these are incompatible with online, distributed, or incremental learning settings. This paper presents Adaptive vaRiance Reduction via Online reWeighting (ARROW), the first online SVR algorithm for the MMD and CORAL for streamed data. The method maintains moving average references of the alignment statistics, and adaptively reweights incoming minibatches so that the minibatch and reference statistics are aligned. Further, we propose a relaxed reweighting scheme so that the ensuing weight-optimisation problem is tractable. In experiments and simulations, we show that ARROW performs competitively with offline algorithms in terms of runtime, degree of variance reduction achieved, and target domain accuracy.