Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model's weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.

翻译：广域化是一个转移学习的子领域,目的是在对目标领域缺乏任何了解的情况下缩小两个不同领域之间的差距。我们的方法是在单一源领域培训时解决模型简单化问题。从这个角度出发,我们以一个单一来源培训的深层基础学习模式为基础,构建了一个共同模式,以加强其集体预测的概括化。迄今为止,所取得的结果表明,相对于任何基础学习者而言,组合式的改进是大有希望的。