Search

Model generalization is an important indicator of safety in autonomous driving and medicine. A domain generalization algorithm selection method can guide users to select the most suitable algorithm quickly and accurately. To address the lack of effective algorithm selection methods, a feature contrast (FeCo) selection method based on a domain generalization algorithm based on comparative learning was proposed. Features were selected based on positive and negative example selection strategy. Feature similarity was calculated using the dot product. Finally, the score was calculated by info noise contrastive estimation (InfoNCE). The score was used to evaluate the degree of aggregation of the same class of features and the degree of separation of different class of features, and was verified on a total of 200 domain generalization models in 3 datasets. The experimental results show that FeCo is the only method with stable results among all the methods. The correlation between the results of FeCo and the real generalization error of the model is up to 0. 89, and the running time is shortened by more than 60 times. [ABSTRACT FROM AUTHOR]