In this work, we propose to tackle the problem of domain generalization in
the context of \textit{insufficient samples}. Instead of extracting latent
feature embeddings based on deterministic models, we propose to learn a
domain-invariant representation based on the probabilistic framework by mapping
each data point into probabilistic embeddings. Specifically, we first extend
empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can
measure the discrepancy between mixture distributions (i.e., source domains)
consisting of a series of latent distributions rather than latent points.
Moreover, instead of imposing the contrastive semantic alignment (CSA) loss
based on pairs of latent points, a novel probabilistic CSA loss encourages
positive probabilistic embedding pairs to be closer while pulling other
negative ones apart. Benefiting from the learned representation captured by
probabilistic models, our proposed method can marriage the measurement on the
\textit{distribution over distributions} (i.e., the global perspective
alignment) and the distribution-based contrastive semantic alignment (i.e., the
local perspective alignment). Extensive experimental results on three
challenging medical datasets show the effectiveness of our proposed method in
the context of insufficient data compared with state-of-the-art methods.

探索处理在不足样本情况下的领域泛化问题，通过学习基于概率框架的域不变表示，在全局和局部视角对分布进行测量，以及基于概率的对比语义对齐损失函数，提出了一种解决方法，并通过实验证明其在不足数据环境下相较于现有方法的有效性。