Machine learning systems must adapt to data distributions that evolve over
time, in applications ranging from sensor networks and self-driving car
perception modules to brain-machine interfaces. We consider gradual domain
adaptation, where the goal is to adapt an initial classifier trained on a
source domain given only unlabeled data that shifts gradually in distribution
towards a target domain. We prove the first non-vacuous upper bound on the
error of self-training with gradual shifts, under settings where directly
adapting to the target domain can result in unbounded error. The theoretical
analysis leads to algorithmic insights, highlighting that regularization and
label sharpening are essential even when we have infinite data, and suggesting
that self-training works particularly well for shifts with small
Wasserstein-infinity distance. Leveraging the gradual shift structure leads to
higher accuracies on a rotating MNIST dataset and a realistic Portraits
dataset.

论文研究了机器学习系统对随时间变化而演化的数据分布的适应性，在渐进领域适应的情况下，证明了使用自我训练的非空界，通过正则化和标签增强来提高算法精度，特别是对于 Wasserstein-infinity 距离较小的转化数据和真实肖像数据集。