Fine-tuning pretrained models is a common practice in domain generalization
(DG) tasks. However, fine-tuning is usually computationally expensive due to
the ever-growing size of pretrained models. More importantly, it may cause
over-fitting on source domain and compromise their generalization ability as
shown in recent works. Generally, pretrained models possess some level of
generalization ability and can achieve decent performance regarding specific
domains and samples. However, the generalization performance of pretrained
models could vary significantly over different test domains even samples, which
raises challenges for us to best leverage pretrained models in DG tasks. In
this paper, we propose a novel domain generalization paradigm to better
leverage various pretrained models, named specialized ensemble learning for
domain generalization (SEDGE). It first trains a linear label space adapter
upon fixed pretrained models, which transforms the outputs of the pretrained
model to the label space of the target domain. Then, an ensemble network aware
of model specialty is proposed to dynamically dispatch proper pretrained models
to predict each test sample. Experimental studies on several benchmarks show
that SEDGE achieves significant performance improvements comparing to strong
baselines including state-of-the-art method in DG tasks and reduces the
trainable parameters by ~99% and the training time by ~99.5%.

本文提出一种新的域泛化范式 —— 专业组合学习，利用固定的预先训练模型，先训练出一个线性标签空间适配器，然后提出一个认识到模型特殊性的集成网络，动态地分配适当的预训练模型来预测每个测试样本，与现有的域泛化方法相比，可以显著提高性能并大大减少可训练参数和时间成本。