International business machines corporation (20240176999). AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS simplified abstract
Contents
- 1 AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS - 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
AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS
Organization Name
international business machines corporation
Inventor(s)
Omer Yehuda Boehm of Haifa (IL)
AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240176999 titled 'AUTOMATIC DATA FABRICATION BY COMBINING GENERATIVE ADVERSARIAL NETWORKS
Simplified Explanation
The computer-implemented method described in the abstract involves training two generative adversarial networks (GANs) based on original and fabricated structured data, combining them into a single GAN, and using it to generate new fabricated data that imitate characteristics of the original data while adhering to user-defined constraints.
- Training a first GAN on original structured data
- Training a second GAN on fabricated structured data adhering to user-defined constraints
- Combining the first and second GANs into a combined GAN
- Training the combined GAN
- Operating the trained combined GAN to generate new fabricated data
Potential Applications
This technology could be applied in various fields such as data augmentation, synthetic data generation for training machine learning models, and data privacy protection.
Problems Solved
This technology addresses the need for generating realistic synthetic data that mimics original data while meeting specific constraints, which can be useful in scenarios where real data is limited or sensitive.
Benefits
The benefits of this technology include enhanced data generation capabilities, improved model training outcomes, increased data privacy, and the ability to explore different data scenarios without relying solely on real-world data.
Potential Commercial Applications
One potential commercial application of this technology could be in the healthcare industry for generating synthetic patient data for research and development purposes.
Possible Prior Art
Prior art in this field may include research on generative adversarial networks, data augmentation techniques, and synthetic data generation methods.
Unanswered Questions
How does this technology compare to existing data generation methods?
This article does not provide a direct comparison with other data generation techniques, leaving room for further exploration of its advantages and limitations.
What are the computational requirements for training and operating the combined GAN?
The abstract does not specify the computational resources needed for implementing this method, which could be crucial for practical applications.
Original Abstract Submitted
a computer-implemented method including: training a first generative adversarial network (gan) based on original structured data; training a second gan based on fabricated structured data that adhere to user-defined constraints; combining the first and second gans into a combined gan; training the combined gan; and operating the trained combined gan to generate new fabricated data that both imitate characteristics of the original structured data, and adhere to the user-defined constraints.
