Domain generalization is a critical challenge for machine learning systems.
Prior domain generalization methods focus on extracting domain-invariant
features across several stationary domains to enable generalization to new
domains. However, in non-stationary tasks where new domains evolve in an
underlying continuous structure, such as time, merely extracting the invariant
features is insufficient for generalization to the evolving new domains.
Nevertheless, it is non-trivial to learn both evolving and invariant features
within a single model due to their conflicts. To bridge this gap, we build
causal models to characterize the distribution shifts concerning the two
patterns, and propose to learn both dynamic and invariant features via a new
framework called Mutual Information-Based Sequential Autoencoders (MISTS).
MISTS adopts information theoretic constraints onto sequential autoencoders to
disentangle the dynamic and invariant features, and leverage a domain adaptive
classifier to make predictions based on both evolving and invariant
information. Our experimental results on both synthetic and real-world datasets
demonstrate that MISTS succeeds in capturing both evolving and invariant
information, and present promising results in evolving domain generalization
tasks.

使用因果模型通过互信息序列自动编码器（MISTS）学习动态特征和不变特征，以解决在非静态任务中从连续结构中提取不变特征并进行扩展域泛化的挑战。