Synthetic Image Data Generation for ML Context Adaptation
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Solution Overview
Problem
Machine learning models trained in one context struggle to accurately analyze data from a different context, leading to disappointing results due to differences in environmental conditions such as temperature, humidity, and geographical region, limiting their applicability across various scenarios.
Innovation Solution
A computer-implemented method generates synthetic image data using a generative model to convert evaluation image data from an incompatible format to a compatible format with the existing machine learning model, allowing the model to provide accurate analysis by adapting to new contexts through domain adaptation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained with images from a specific context (region, time, climate), then the model achieves high accuracy for that context, but the model performs poorly when applied to data from different contexts
Solution Approach 1:
The patent introduces synthetic data as an intermediary between the original training data and the evaluation data from different contexts. The synthetic data is generated to bridge the domain gap, allowing the model to adapt to new contexts without requiring extensive retraining on actual data from those contexts. This mediator enables knowledge transfer across different domains while maintaining model accuracy.
Solution Approach 2:
The patent employs domain adaptation techniques that modify model parameters based on the target context. By adjusting parameters when transitioning between different contexts (regions, times, climates), the model maintains high accuracy across diverse evaluation scenarios while preserving the core learning from original training data.
2Adaptability or versatility
If synthetic image data is generated using domain adaptation techniques, then the model can accurately analyze data from different contexts, but the system complexity increases due to additional generative models and format conversion processes
Solution Approach 1:
The patent creates synthetic copies of training data that are adapted to different contexts. Instead of modifying the original data or building complex adaptation systems for each new context, the system generates copied versions of the training data with adjusted characteristics matching the target context, simplifying the overall system architecture while maintaining versatility.
Data Source
AI summary
A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.