Domain generalization aims to improve the generalization capacity of a model by leveraging useful information from the multi-domain data. However, learning an effective feature representation from such multi-domain data is challenging, due to the domain shift problem. In this paper, we propose an information gating strategy, termed cross-domain gating (CDG), to address this problem. Specifically, we try to distill the domain-invariant feature by adaptively muting the domain-related activations in the feature maps. This feature distillation process prevents the network from overfitting to the domain-related detailed information, and thereby improves the generalization ability of learned feature representation. Extensive experiments are conducted on three public datasets. The experimental results show that the proposed CDG training strategy can excellently enforce the network to exploit the intrinsic features of objects from the multi-domain data, and achieve a new state-of-the-art domain generalization performance on these benchmarks.
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence,based on the mathematical algorithm model,the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution.And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator,the improved operator can satisfy the need of integer coding in drilling path optimization.The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize,fast convergence speed,and better global convergence characteris- tics.hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes. 相似文献