17933348. GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING simplified abstract (Dell Products L.P.)
Contents
- 1 GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING - 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 Original Abstract Submitted
GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
Organization Name
Inventor(s)
Rômulo Teixeira de Abreu Pinho of Niterói (BR)
GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17933348 titled 'GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
Simplified Explanation
The abstract describes a patent application for a machine learning model training system that uses real and/or synthetic data contributed by nodes to a central machine learning service. The models are trained based on the data and can generate synthetic data according to a node's distribution. When a node is unavailable, synthetic data from an enabled generator is used for retraining the model.
- Nodes contribute data to a central machine learning service.
- Data is used to train models that can generate synthetic data.
- Synthetic data is used when a node is unavailable for retraining.
- The system aims to improve machine learning model training with a combination of real and synthetic data.
Potential Applications
This technology could be applied in various industries such as finance, healthcare, and marketing for improving machine learning model training processes.
Problems Solved
1. Improved efficiency in retraining machine learning models when nodes are unavailable. 2. Enhanced data diversity by incorporating synthetic data into the training process.
Benefits
1. Increased accuracy of machine learning models. 2. Reduced downtime when nodes are unavailable. 3. Enhanced data diversity for better model performance.
Potential Commercial Applications
"Enhancing Machine Learning Model Training with Real and Synthetic Data" could be used in industries such as finance for fraud detection, healthcare for patient diagnosis, and marketing for customer segmentation.
Possible Prior Art
One possible prior art could be the use of synthetic data in machine learning model training to improve performance and data diversity.
Unanswered Questions
How does this technology handle data privacy concerns?
The article does not address how the system ensures the privacy and security of the data contributed by nodes and generated by the models.
What computational resources are required to implement this system?
The article does not provide information on the computational resources needed to deploy and operate this machine learning model training system.
Original Abstract Submitted
Machine learning model training using real and/or synthetic data is disclosed. Nodes contribute data to a central machine learning service. The data is used to train corresponding models whose generators, when trained, are configured to generate synthetic data according to a node's distribution. When a node is unavailable or for other reasons, the data contributed by the node for retraining a machine learning model includes at least some synthetic data from an enabled generator.
