2results about How to "Solve overfitting" patented technology

The application discloses a subject-driven personalized generation method based on decoupled mask prompt attention fine-tuning, and belongs to the field of deep learning, computer vision and artificial intelligence generated content. A mask prompt decoupling module is designed to decompose the unified text prompt into a text prompt containing only the subject and a text prompt containing only the context. A subject attention focusing module and a context attention adjusting module are introduced, and independent subject feature extraction paths and context semantic adaptation paths are established, respectively. The subject identity learning and the context scene modeling are explicitly separated, and the double constraint loss guided by the mask is used for joint optimization to further prevent feature coupling. Finally, the algorithm effectively preserves the fine appearance features of the subject, significantly enhances the adaptability of the model to novel context instructions, and effectively improves the quality of personalized generation and the flexibility of context editing.

This disclosure relates to the field of face recognition technology, and provides a training method and apparatus for a face recognition model that avoids the long tail problem of data. The method includes: constructing a face recognition model; obtaining a training dataset, and executing the following loop to train the face recognition model in multiple rounds: sampling the current round of training from the training dataset using a dynamic sampler to obtain a sample set used for the current round of training; inputting the sample set into a feature extraction network to obtain a feature vector set corresponding to the sample set; inputting the feature vector set into a normalization network to normalize the feature vectors in the feature vector set; calculating a classification loss using a classification network and a contrastive loss using a contrastive network based on the feature vector set processed by the normalization network; updating the model parameters of the face recognition model based on the classification loss and contrastive loss; incrementing the training round number corresponding to the current round of training by one; and ending the loop when the training round number equals a preset round number.