Balanced Synthetic Data Generation via GAN Feedback
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Solution Overview
Problem
Imbalanced synthetic training data can lead to biased machine learning models in communication systems, particularly in optimizing resource utilization and detecting intrusions, as existing methods fail to generate sufficiently balanced datasets for effective training.
Innovation Solution
An apparatus and method utilizing competing generative adversarial networks to iteratively refine synthetic data generation by adjusting trainable parameters based on feedback from a discriminator model, ensuring accuracy meets a preset threshold and an end criterion, thereby producing balanced training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic training data is generated to obtain enough training data, then the quantity of training data is improved, but the balance of training data deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where a discriminator model evaluates the quality and distribution of synthetic data generated by a generator model. The discriminator's feedback on data distribution balance guides the generator to produce more balanced synthetic data, resolving the contradiction between quantity and balance of training data.
Solution Approach 2:
The discriminator model serves as an intermediary between the generator and the training process. It mediates the data generation process by evaluating whether synthetic data maintains proper distribution balance, allowing the system to generate sufficient quantity of data while preserving balance through the discriminator's guidance.
2Ease of manufacture
If imbalanced training data is used, then the training process is simplified, but the model accuracy deteriorates
Solution Approach 1:
The system performs self-service by automatically generating balanced synthetic data through the generator-discriminator framework. The discriminator evaluates data balance and provides feedback, enabling the system to self-correct imbalances without manual intervention, thus maintaining both simplicity and accuracy.
3Productivity
If synthetic data is generated without balancing, then the productivity is improved, but the reliability of trained models deteriorates
Solution Approach 1:
The system performs preliminary action by generating and evaluating synthetic data through the discriminator before final training. This preliminary evaluation ensures data balance is established beforehand, allowing fast generation while maintaining reliability through pre-validated data quality.
Data Source
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AI summary
Two competing machine learning based models, a first model for generating synthetic data, and a second model for classifying input data to synthetic data and real data, are trained by training the second model until its accuracy meets a preset rule, and then the first model is trained. After training the first model, training of models is repeated until an end criterium is met.