Huawei technologies co., ltd. (20240265256). MODEL TRAINING METHOD AND RELATED DEVICE simplified abstract
Contents
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 20240265256 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 first data sequence with a first model to obtain a first feature sequence.
- Processing the first data sequence with a second model to obtain a second feature sequence.
- Training the first and second 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 model accuracy by training models with perturbed data sequences.
- Benefits:**
The benefits of this technology include enhanced model accuracy, improved data labeling, and better performance in various machine learning tasks.
- Commercial Applications:**
This technology has commercial applications in industries such as healthcare, finance, and e-commerce for tasks like image classification, sentiment analysis, and fraud detection.
- Prior Art:**
Prior research in machine learning and data augmentation techniques can provide insights into similar methods used in the past.
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms, data augmentation techniques, and model training methods to enhance the effectiveness of this technology.
- Questions about Model Training with Perturbed Data Sequences:**
1. How does training models with perturbed data sequences improve accuracy in labeling data? 2. What are the potential challenges in implementing this method in real-world applications?
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.
