20230196810. NEURAL ODE-BASED CONDITIONAL TABULAR GENERATIVE ADVERSARIAL NETWORK APPARATUS AND METHOD simplified abstract (UIF (University Industry Foundation), Yonsei University)
NEURAL ODE-BASED CONDITIONAL TABULAR GENERATIVE ADVERSARIAL NETWORK APPARATUS AND METHOD
Organization Name
UIF (University Industry Foundation), Yonsei University
Inventor(s)
Jae Hoon Lee of Tongyeong-si (KR)
Ji Hyeon Hyeong of Jeju-si (KR)
NEURAL ODE-BASED CONDITIONAL TABULAR GENERATIVE ADVERSARIAL NETWORK APPARATUS AND METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230196810 titled 'NEURAL ODE-BASED CONDITIONAL TABULAR GENERATIVE ADVERSARIAL NETWORK APPARATUS AND METHOD
Simplified Explanation
The abstract describes a patent application for a neural ode-based conditional tabular generative adversarial network apparatus. This apparatus includes three main components: a tabular data preprocessing unit, a neural ordinary differential equation (node)-based generation unit, and a node-based discrimination unit.
- The tabular data preprocessing unit is responsible for preparing the tabular data, which consists of a discrete column and a continuous column, for further processing.
- The neural ordinary differential equation (node)-based generation unit utilizes a condition vector and a noisy vector generated from the preprocessed tabular data to generate a fake sample.
- The node-based discrimination unit receives either a real sample or the fake sample of the preprocessed tabular data and performs continuous trajectory-based classification.
Potential applications of this technology:
- Data generation: The apparatus can be used to generate synthetic tabular data that closely resembles real data, which can be useful for various applications such as data augmentation, privacy protection, and testing machine learning models.
- Anomaly detection: By training the apparatus on real tabular data, it can be used to identify anomalies or outliers in new data by comparing the generated fake samples with the real samples.
- Data analysis: The apparatus can assist in exploring and understanding tabular data by generating diverse samples that capture the underlying patterns and distributions.
Problems solved by this technology:
- Limited data availability: The apparatus can generate synthetic tabular data when real data is scarce or restricted, enabling the development and testing of machine learning models in data-limited scenarios.
- Privacy concerns: By generating synthetic data that closely resembles real data, the apparatus can help protect sensitive information while still allowing for analysis and model development.
- Data exploration and understanding: The apparatus provides a tool for generating diverse samples that can aid in exploring and understanding the patterns and distributions within tabular data.
Benefits of this technology:
- Improved data availability: The apparatus allows for the generation of synthetic tabular data, expanding the availability of data for various applications.
- Enhanced privacy protection: By generating synthetic data, the apparatus helps address privacy concerns by reducing the need for direct access to sensitive real data.
- Efficient data analysis: The apparatus provides a means to explore and understand tabular data by generating diverse samples, facilitating more comprehensive analysis and model development.
Original Abstract Submitted
a neural ode-based conditional tabular generative adversarial network apparatus includes: a tabular data preprocessing unit for preprocessing tabular data composed of a discrete column and a continuous column; a neural ordinary differential equation (node)-based generation unit for generating a fake sample by reading a condition vector and a noisy vector generated based on the preprocessed tabular data; and a node-based discrimination unit for receiving a sample composed of a real sample or the fake sample of the preprocessed tabular data and performing continuous trajectory-based classification.
