18617095. MODEL TRAINING METHOD AND RELATED DEVICE simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)
Contents
- 1 MODEL TRAINING METHOD AND RELATED DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL TRAINING METHOD AND RELATED DEVICE - 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 Questions about the Technology
- 1.11 Original Abstract Submitted
MODEL TRAINING METHOD AND RELATED DEVICE
Organization Name
Inventor(s)
Wenyong Huang of Shenzhen (CN)
Yu Ting Yeung of Hong Kong (CN)
MODEL TRAINING METHOD AND RELATED DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18617095 titled 'MODEL TRAINING METHOD AND RELATED DEVICE
Simplified Explanation
This patent application describes a method for training models using perturbed data sequences to improve accuracy in labeling data.
Key Features and Innovation
- Obtaining a first data sequence and a perturbed first data sequence.
- Processing the perturbed data sequence with one model and the original data sequence with another model to obtain feature sequences.
- Training both models based on the feature sequences to obtain target models.
- Fine-tuning the target models to improve accuracy in labeling data sequences.
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved
This technology addresses the challenge of improving the accuracy of model training by using perturbed data sequences.
Benefits
- Enhanced accuracy in labeling data sequences.
- Improved performance of machine learning models.
- Increased efficiency in model training processes.
Commercial Applications
- Image recognition software for security systems.
- Sentiment analysis tools for social media monitoring.
- Predictive maintenance solutions for industrial equipment.
Questions about the Technology
How does this technology improve model training processes?
This technology improves model training processes by using perturbed data sequences to enhance the accuracy of labeling data.
What are the potential applications of this technology beyond model training?
This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics to improve performance and efficiency.
Original Abstract Submitted
This application provides a model training method and a related device. The method in this application includes: obtaining a first data sequence and a perturbed first data sequence; processing the perturbed first data sequence by using a first to-be-trained model to obtain a first feature sequence, and processing the first data sequence by using a second to-be-trained model to obtain a second feature sequence; training the first to-be-trained model and the second to-be-trained model based on the first feature sequence and the second feature sequence to obtain a first target model and a second target model; and fine-tuning the first target model or the second target model to obtain a third target model, where the third target model is used to obtain a label of a data sequence.
