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: this https URL

提出一种名为 “动态域泛化”(DDG) 的新颖域泛化变体，其中模型学习扭曲网络参数以适应来自不同域的数据，并使用元调整器来扭曲网络参数，以便针对不同来源和目标域进行动态调整。通过使用 DomainMix 来模拟不同域的数据进行教学，以帮助元调整器适应未来的未知目标领域，无需经过训练即可实现模型的自适应调整，实验证明该方法的有效性。