Adversarial Data Style Transformation with Error Preservation
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Solution Overview
Problem
Machine learning models require significant amounts of training data that closely resemble the data they will process, but existing methods struggle to generate data in specific styles, such as high-quality images or scanned text images, which are necessary for tasks like OCR, due to the lack of errors and artifacts in computer-generated data.
Innovation Solution
A system comprising a generator model, a discriminator model, and a preserver model that transforms data from a first style to a second style, with the discriminator model distinguishing between the two styles and the preserver model measuring information retention, allowing the generator model to optimize resemblance and information measures to produce high-quality converted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computer-generated data is used for training, then data availability is improved, but data quality and realism are worsened due to lack of errors and artifacts
Solution Approach 1:
A generator model is introduced as an intermediary between the discriminator model and the training data. The generator model transforms computer-generated data into synthetic data that mimics real-world characteristics including errors and artifacts, thereby mediating between the availability of computer-generated data and the quality requirements of training data
Solution Approach 2:
A discriminator model is employed to provide feedback to the generator model by evaluating whether generated data resembles real data. This feedback loop enables iterative improvement of the generator model, allowing it to progressively enhance the realism and quality of synthetic training data while maintaining data availability
2Manufacturing precision
If data style transformation is implemented, then data quality is improved, but system complexity increases due to multiple models
Solution Approach 1:
The generator model serves multiple functions: it transforms data between different styles, generates synthetic training data, and adapts computer-generated data to match real-world characteristics. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while improving data quality
Solution Approach 2:
The system transforms data by changing parameters such as style characteristics, error patterns, and artifact distributions. By adjusting these parameters, the generator model can produce high-quality training data that matches specific requirements without requiring fundamentally different system architectures
Data Source
AI summary
Systems and methods for transforming data between multiple styles are provided. In one embodiment, a system is provided that includes a generator model, a discriminator model, and a preserver model. The generator model may be configured to receive data in a first style and generate converted data in a second style. The discriminator model may be configured to receive the converted data from the generator model, compare the converted data to original data in the second style, and compute a resemblance measure based on the comparison. The preserver model may be configured to receive the converted data from the generator model and compute an information measure of the converted data. The generator model may also be trained to optimize the resemblance measure and the information measure.