While large volumes of unlabeled data are usually available, associated
labels are often scarce. The unsupervised domain adaptation problem aims at
exploiting labels from a source domain to classify data from a related, yet
different, target domain. When time series are at stake, new difficulties arise
as temporal shifts may appear in addition to the standard feature distribution
shift. In this paper, we introduce the Match-And-Deform (MAD) approach that
aims at finding correspondences between the source and target time series while
allowing temporal distortions. The associated optimization problem
simultaneously aligns the series thanks to an optimal transport loss and the
time stamps through dynamic time warping. When embedded into a deep neural
network, MAD helps learning new representations of time series that both align
the domains and maximize the discriminative power of the network. Empirical
studies on benchmark datasets and remote sensing data demonstrate that MAD
makes meaningful sample-to-sample pairing and time shift estimation, reaching
similar or better classification performance than state-of-the-art deep time
series domain adaptation strategies.

在这篇论文中，我们介绍了 Match-And-Deform（MAD）方法，它旨在寻找源时间序列和目标时间序列之间的对应关系，同时允许时间扭曲。通过最优输运损失和动态时间扭曲，相关的优化问题可以同时对齐时间序列和时间戳。将 MAD 嵌入到深度神经网络中，有助于学习时间序列的新表示方法，既能对齐不同领域的时间序列，又能最大限度地提高网络的判别能力。基准数据集和遥感数据的实证研究表明，MAD 能够进行有意义的样本配对和时间偏移估计，并达到与最先进的深度时间序列领域自适应策略相似或更好的分类性能。