Image Augmentation via Mean-Field Games for Shape-Preserving Diversity
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Solution Overview
Problem
Existing machine learning systems require diverse and abundant training data to maintain accuracy and predictability, but current data augmentation methods are inadequate in providing sufficient diversity and affinity.
Innovation Solution
A system and method using mean-field games to perform time-continuous transformations of pixel or feature distributions between images or datasets, generating augmented datasets that retain the shape of the original images and enhance diversity and affinity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data augmentation methods are used, then data volume increases, but data diversity and affinity are insufficient
Solution Approach 1:
The patent applies dynamic optimal transport plans that adaptively transform data distributions along continuous paths. Instead of static augmentation rules, the system dynamically computes transport maps that evolve over time, allowing data to transform smoothly from source to target distribution while preserving semantic relationships and enhancing diversity.
Solution Approach 2:
The system changes the parameters of data distributions by computing optimal transport between different distribution parameters. By adjusting distribution parameters along continuous paths and using entropy regularization, the method transforms data characteristics to achieve both volume increase and improved diversity/affinity.
2Reliability
If more training data is provided, then machine learning accuracy improves, but data acquisition and processing complexity increases
Solution Approach 1:
The patent creates synthetic copies of training data through optimal transport transformations. Instead of acquiring and processing additional real data, the system generates augmented samples by transporting existing data along computed optimal paths, reducing the complexity of data acquisition while maintaining accuracy improvements.
Solution Approach 2:
The optimal transport plan serves as an intermediary that systematically transforms source data into augmented target data. This mediator structure provides a mathematically grounded transformation process that is more efficient and less complex than traditional manual or heuristic-based augmentation methods.
3Adaptability or versatility
If data transformation is performed to increase diversity, then data affinity may be lost, but maintaining shape consistency requires constrained transformation
Solution Approach 1:
The patent uses dynamic optimal transport with continuous-time formulations that allow controlled evolution of data transformations. By parameterizing transport paths continuously and using entropy regularization, the system dynamically balances diversity enhancement with shape preservation, transforming data gradually along optimal paths rather than through abrupt changes.
Solution Approach 2:
The entropy regularization term provides feedback control during the transport process, preventing excessive transformations that would lose shape information while still achieving diversity. The feedback mechanism monitors transformation progress and adjusts the transport plan to maintain affinity and shape consistency.
Data Source
AI summary
A system for image augmentation includes a processor and a memory. The memory includes instructions stored thereon, which when executed by the processor cause the system to access a first image and a second image, generate a path including points, perform a time-continuous transformation of a first distribution of pixels of the first image to a second distribution of pixels of the second image within a time interval along the path based on a mean-field game, and generate an augmented dataset based on the time-continuous transformation. The points start from the first image and end at the second image. The points include augmented images. The augmented images retain a shape of the first image and the second image.