  Domain generalization (DG) is a fundamental yet very challenging research
topic in machine learning. The existing arts mainly focus on learning
domain-invariant features with limited source domains in a static model.
Unfortunately, there is a lack of training-free mechanism to adjust the model
when generalized to the agnostic target domains. To tackle this problem, we
develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in
which the model learns to twist the network parameters to adapt the data from
different domains. Specifically, we leverage a meta-adjuster to twist the
network parameters based on the static model with respect to different data
from different domains. In this way, the static model is optimized to learn
domain-shared features, while the meta-adjuster is designed to learn
domain-specific features. To enable this process, DomainMix is exploited to
simulate data from diverse domains during teaching the meta-adjuster to adapt
to the upcoming agnostic target domains. This learning mechanism urges the
model to generalize to different agnostic target domains via adjusting the
model without training. Extensive experiments demonstrate the effectiveness of
our proposed method. Code is available at: https://github.com/MetaVisionLab/DDG
