Nec corporation (20240135143). MODEL GENERATION SYSTEM, METHOD, AND PROGRAM simplified abstract
Contents
- 1 MODEL GENERATION SYSTEM, METHOD, AND PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL GENERATION SYSTEM, METHOD, AND PROGRAM - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
MODEL GENERATION SYSTEM, METHOD, AND PROGRAM
Organization Name
Inventor(s)
MODEL GENERATION SYSTEM, METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135143 titled 'MODEL GENERATION SYSTEM, METHOD, AND PROGRAM
Simplified Explanation
The patent application describes a system that uses neural networks to generate false data and determine the likeness of the data to true data. Here are the key points of the invention:
- Data generation unit creates false data using a neural network generation model.
- Discriminator unit evaluates the likeness of given data to true data using a neural network discriminator model.
- Gradient information calculation unit computes update amounts for the discriminator model weights based on the difference between output values for true and false data.
- Selection unit chooses false data to be stored in a saved data storage unit.
Potential Applications
The technology described in the patent application could be applied in various fields such as:
- Data augmentation for machine learning training datasets.
- Synthetic data generation for privacy-preserving applications.
Problems Solved
The system addresses the following issues:
- Generating diverse and realistic false data for training machine learning models.
- Improving the performance of discriminator models in distinguishing between true and false data.
Benefits
The benefits of this technology include:
- Enhanced data diversity for training models.
- Improved accuracy in detecting false data.
Potential Commercial Applications
A potential commercial application for this technology could be in:
- Data science and machine learning software tools development.
Possible Prior Art
One possible prior art for this technology could be:
- Generative Adversarial Networks (GANs) used for generating synthetic data.
Unanswered Questions
How does the system handle imbalanced datasets in the training process?
The patent application does not provide information on how the system deals with imbalanced datasets during training.
What is the computational complexity of the gradient information calculation unit?
The patent application does not discuss the computational complexity of the gradient information calculation unit.
Original Abstract Submitted
a data generation unit generates a first number of false data based on a generation model that is a neural network for generating false data. a discriminator unit derives output values for given data based on a discriminator model that is a neural network for deriving output values indicating true data-likeness and false data-likeness of the given data. a gradient information calculation unit calculates, for each combination of one true data and each of the first number of false data, a distance between the output value for the true data and the output value for the false data, and calculates gradient information that is an update amount for each weight that the discriminator model has, so as to increase the distance by a predetermined amount. a selection unit selects false data to be stored in the saved data storage unit
