Supervised learning results typically rely on assumptions of i.i.d. data.
Unfortunately, those assumptions are commonly violated in practice. In this
work, we tackle such problem by focusing on domain generalization: a
formalization where the data generating process at test time may yield samples
from never-before-seen domains (distributions). Our work relies on the
following lemma: by minimizing a notion of discrepancy between all pairs from a
set of given domains, we also minimize the discrepancy between any pairs of
mixtures of domains. Using this result, we derive a generalization bound for
our setting. We then show that low risk over unseen domains can be achieved by
representing the data in a space where (i) the training distributions are
indistinguishable, and (ii) relevant information for the task at hand is
preserved. Minimizing the terms in our bound yields an adversarial formulation
which estimates and minimizes pairwise discrepancies. We validate our proposed
strategy on standard domain generalization benchmarks, outperforming a number
of recently introduced methods. Notably, we tackle a real-world application
where the underlying data corresponds to multi-channel electroencephalography
time series from different subjects, each considered as a distinct domain.

本文探讨监督学习中数据导致的实际问题，着重于领域泛化研究，提出最小化给定领域对之间差异的方法，并在 EEG 数据集中验证了该策略。