Huawei technologies co., ltd. (20240185086). MODEL DISTILLATION METHOD AND RELATED DEVICE simplified abstract
Contents
- 1 MODEL DISTILLATION METHOD AND RELATED DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL DISTILLATION METHOD AND RELATED DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Frequently Updated Research
- 1.11 Original Abstract Submitted
MODEL DISTILLATION METHOD AND RELATED DEVICE
Organization Name
Inventor(s)
Lifeng Shang of Hong Kong (CN)
MODEL DISTILLATION METHOD AND RELATED DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185086 titled 'MODEL DISTILLATION METHOD AND RELATED DEVICE
Simplified Explanation
The patent application relates to model distillation methods and apparatuses in the field of artificial intelligence.
- Obtaining first input data and second input data from a second computing node.
- Processing the first input data using the first sub-model to obtain a first intermediate output.
- Processing the second input data using the second sub-model to obtain a second intermediate output.
- Using the first intermediate output and the second intermediate output to determine a first gradient.
- Distilling the first sub-model based on the first gradient to obtain an updated first sub-model.
Potential Applications
This technology could be applied in various industries such as healthcare, finance, and autonomous vehicles for improving model efficiency and performance.
Problems Solved
This technology helps in distilling complex models into simpler ones, making them more efficient and easier to deploy in real-world applications.
Benefits
The benefits of this technology include improved model efficiency, reduced computational resources, and faster inference times.
Potential Commercial Applications
Potential commercial applications of this technology include model optimization services for businesses, AI software development companies, and research institutions.
Possible Prior Art
Prior art in the field of model distillation includes research papers on knowledge distillation techniques and model compression methods.
What are the limitations of this technology in real-world applications?
One limitation of this technology is the potential loss of accuracy when distilling complex models into simpler ones.
How does this technology compare to existing model distillation methods?
This technology offers a more efficient and effective way of distilling models compared to traditional knowledge distillation techniques.
Frequently Updated Research
One frequently updated research topic related to this technology is the development of novel distillation algorithms for improving model compression and performance.
Original Abstract Submitted
this disclosure relates to the field of artificial intelligence, and provides model distillation methods and apparatuses. in an implementation, a method including: obtaining first input data and second input data from a second computing node, wherein the first input data is output data of the third sub-model, and the second input data is output data processed by the fourth sub-model, processing the first input data by using the first sub-model, to obtain a first intermediate output, processing the second input data by using the second sub-model, to obtain a second intermediate output, wherein the first intermediate output and the second intermediate output are used to determine a first gradient, and distilling the first sub-model based on the first gradient, to obtain an updated first sub-model.
