Synthetic Training Data Generation for ML Generalizability
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Solution Overview
Problem
Machine learning models face limitations in generalizability due to inadequate training data quality, volume, variety, and velocity, leading to poor performance in real-world scenarios.
Innovation Solution
A system that generates synthetic training data by augmenting annotated source images through element insertion, modality variation, and geometric transformations, creating diverse and voluminous training images to enhance model generalizability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world annotated training data is collected and used, then model training accuracy is improved, but data volume and variety are limited
Solution Approach 1:
The system creates synthetic copies of annotated training data by generating new images with inserted elements of interest and background elements. These synthetic copies preserve the annotation quality of original data while multiplying the available training volume through automated generation processes
Solution Approach 2:
The system varies multiple parameters in generated training data including element positions, sizes, orientations, background configurations, and image characteristics. This parameter variation maintains data quality while exponentially increasing data variety and volume through controlled transformations
2Adaptability or versatility
If real-world annotated training data is collected, then data variety is improved, but data collection time and cost increase
Solution Approach 1:
The system performs preliminary data preparation by pre-defining libraries of elements of interest and background elements with various parameters. This preliminary setup enables rapid generation of diverse training data without time-consuming real-world collection and annotation processes
Solution Approach 2:
The system replicates diverse training scenarios through synthetic generation rather than physical collection. By copying and transforming existing annotated data with varied parameter combinations, the system achieves high data variety instantaneously without extended collection timelines
3Reliability
If more training data is generated through augmentation, then model generalizability is improved, but system complexity increases
Solution Approach 1:
The system segments the data generation process into distinct modular components: element insertion module, background generation module, parameter variation module, and annotation preservation module. This segmentation manages complexity by making each component independent and interchangeable while collectively achieving high model generalizability
Data Source
AI summary
Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images. In various instances, a deployable annotated training image can be formed by varying at least one geometric characteristic of an intermediate annotated training image. In various embodiments, a training component can train a machine learning model on the set of deployable annotated training images.