基于特征重组的图像识别模型的训练方法及装置、图像识别方法、设备、介质及产品
By training with feature recombination and knowledge distillation, the feature distribution of the student model is adjusted to approximate that of the teacher model. This solves the problem of inaccurate image recognition caused by the difference in feature distribution at different granularities between the teacher and student models, and improves the overall and detailed feature recognition performance of image recognition.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF AUTOMATION CHINESE ACAD OF SCI
- Filing Date
- 2026-06-17
- Publication Date
- 2026-07-17
AI Technical Summary
Due to the difference in feature distribution between the teacher model and the student model at different granularities, the final trained student model is unable to achieve more accurate image recognition, especially when the image features are complex, making it difficult to accurately identify both the overall features and the detailed features of the image at the same time.
By analyzing the intermediate feature distributions of the teacher and student models, the feature distribution of the student model is adjusted to approximate that of the teacher model. Feature recombination methods, including feature decomposition and weighted recombination, are used, combined with knowledge distillation training, to optimize the feature distribution of the student model.
This improved the student model's ability to recognize both overall and detailed features of images, making the final trained student model closer to the teacher model's image recognition capabilities and achieving more accurate image recognition.
Smart Images
Figure CN122416218A_ABST