Domain generalization method based on improved meta-learning adversarial domain enhancement

The invention relates to the field of machine learning domain generalization, and particularly discloses a domain generalization method based on improved meta-learning adversarial domain enhancement, and the method comprises the steps: reconstructing a feature extraction network structure, introducing a WGAN-GP generation adversarial network, and carrying out the data standardization through a self-normalization and cross-normalization method. A good effect can be achieved by generalizing a model trained in a single training domain to an unknown field. Besides, the model provided by the invention has relatively high correct recognition rate and generalization ability, is relatively small in size, and can stably and efficiently operate in a computing environment with limited resources. Therefore, the method is suitable for being quickly and widely deployed in a domain generalization application, and the performance and the working efficiency of domain generalization are effectively improved. 本发明涉及机器学习域泛化领域，具体公