HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Generative adversarial networks (GANs) are widely used for synthetic data generation.
• Existing methods often prioritize individual sample realism.
• Real-world applications require additional synthetic data constraints like privacy and diversity.

Purpose of the Study:

• Introduce HydraGAN, a novel multi-agent network for multi-objective synthetic data generation.
• Address limitations of current methods in balancing multiple data generation criteria.
• Provide a framework for optimizing synthetic data beyond simple realism.

Main Methods:

• Developed HydraGAN, a multi-agent system with one generator and multiple discriminators.
• Theoretically verified that HydraGAN training converges to a Nash equilibrium.
• Evaluated HydraGAN on six diverse datasets.

Main Results:

• HydraGAN effectively balances multiple data generation objectives.
• The Area under the Radar Curve (AuRC) was maximized by HydraGAN.
• Experimental results demonstrate superior performance compared to prior methods across datasets.

Conclusions:

• HydraGAN offers a robust solution for multi-objective synthetic data generation.
• The multi-agent approach enables balancing of cooperative and competitive data generation goals.
• HydraGAN advances the field of synthetic data generation for complex requirements.