Invariance-principle-based methods such as Invariant Risk Minimization (IRM),
have recently emerged as promising approaches for Domain Generalization (DG).
Despite promising theory, such approaches fail in common classification tasks
due to the mixing of true invariant features and spurious invariant features.
To address this, we propose a framework based on the conditional entropy
minimization (CEM) principle to filter-out the spurious invariant features
leading to a new algorithm with a better generalization capability. We show
that our proposed approach is closely related to the well-known Information
Bottleneck (IB) framework and prove that under certain assumptions, entropy
minimization can exactly recover the true invariant features. Our approach
provides competitive classification accuracy compared to recent
theoretically-principled state-of-the-art alternatives across several DG
datasets.

提出了一种基于条件熵最小化原则的框架，用于过滤混入小说不变特征的不变特征，以增强新算法的泛化能力。

学习域不变表示特征的条件熵最小化原则

Conditional entropy minimization principle for learning domain invariant representation features

In unsupervised domain adaptation, existing theory focuses on situations
where the source and target domains are close. In practice, conditional entropy
minimization and pseudo-labeling work even when the domain shifts are much
larger than those analyzed by existing theory. We identify and analyze one
particular setting where the domain shift can be large, but these algorithms
provably work: certain spurious features correlate with the label in the source
domain but are independent of the label in the target. Our analysis considers
linear classification where the spurious features are Gaussian and the
non-spurious features are a mixture of log-concave distributions. For this
setting, we prove that entropy minimization on unlabeled target data will avoid
using the spurious feature if initialized with a decently accurate source
classifier, even though the objective is non-convex and contains multiple bad
local minima using the spurious features. We verify our theory for spurious
domain shift tasks on semi-synthetic Celeb-A and MNIST datasets. Our results
suggest that practitioners collect and self-train on large, diverse datasets to
reduce biases in classifiers even if labeling is impractical.

该论文提出了许多方法用于处理不同领域之间的转换，例如通过条件熵最小化和伪标记来完成对这些情况的试验，除此以外还针对某些情况下的结果做出更深入的解释，并验证了这种方法在半合成的 Celeb-A 和 MNIST 数据集上是可行的。