Research on Transfer Learning Method Based on Structural Constraints

In recent years, deep learning technologies have been extensively applied across various domains, particularly in image classification. In traditional deep learning methods, the training data and test data satisfy the same distribution, but the actual situation often fails to meet this assumption. Therefore, the training model obtained at high cost was unable to meet the needs of practical problems. To address this issue, this paper proposes a transfer learning method based on structural constraints, which takes advantages of generative adversarial networks in the field of image translation, and supplemented by image structural constraints to control the meaning of the image expression, so as to carry out knowledge transfer.