Huawei technologies co., ltd. (20240232628). METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS simplified abstract
Contents
- 1 METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Neural Network Model Training
- 1.13 Original Abstract Submitted
METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS
Organization Name
Inventor(s)
METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232628 titled 'METHOD FOR TRAINING NEURAL NETWORK MODEL AND APPARATUS
Simplified Explanation
This patent application describes methods and apparatuses for training neural network models, specifically focusing on improving the precision of a lower-precision model by utilizing data from a higher-precision model.
Key Features and Innovation
- Training a second neural network model using annotation data of a service to improve its precision.
- Updating a first neural network model based on the trained second neural network model.
- Addressing the issue of precision disparity between neural network models.
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and autonomous driving systems.
Problems Solved
This technology addresses the problem of improving the precision of neural network models with varying levels of accuracy.
Benefits
- Enhanced performance of neural network models.
- Improved accuracy in processing services.
- Potential for more efficient and effective AI systems.
Commercial Applications
- Image recognition software development.
- Natural language processing applications.
- Autonomous vehicle technology advancement.
Prior Art
Readers can explore prior research on neural network model training methods and techniques to gain a deeper understanding of the evolution of this technology.
Frequently Updated Research
Stay informed about the latest advancements in neural network model training and precision improvement techniques to remain at the forefront of AI technology.
Questions about Neural Network Model Training
What are the key challenges in training neural network models with varying levels of precision?
Training neural network models with different levels of precision can be challenging due to the need to balance accuracy and computational efficiency. The methods described in this patent application aim to address this challenge by leveraging annotation data to improve the precision of lower-precision models.
How can the updated first neural network model benefit from the trained second neural network model?
The updated first neural network model can benefit from the trained second neural network model by incorporating the improved precision and performance achieved through the training process. This can lead to enhanced overall accuracy and efficiency in processing services.
Original Abstract Submitted
this disclosure provides methods and apparatuses for training a neural network model. one example method performed by a terminal device includes: obtaining annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model, training a second neural network model by using the annotation data of the service to obtain a trained second neural network model, and updating a first neural network model based on the trained second neural network model.
