MLM Artifact Reliance Detection via Synthetic Parameter Shifting
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Solution Overview
Problem
Machine learning models (MLMs) trained with synthetic data often rely on augmentation artifacts, leading to overfitting and poor performance in real-world scenarios, as conventional detection methods fail to distinguish between model performance due to artifacts and genuine data reliance.
Innovation Solution
The approach involves training and testing MLMs with multiple data sets that shift parameters in different directions to detect reliance on artifacts, using reliance indicators and scores to determine the extent of artifact reliance, allowing for corrective actions such as retraining or modifying the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic data is used to train machine learning models, then the availability of training data is improved, but the model reliability deteriorates due to artifact reliance
Solution Approach 1:
The system performs preliminary detection of artifact reliance by testing the model on synthetic data with shifted parameters before deployment. By measuring the difference in model performance between original and shifted synthetic data, the system identifies artifact reliance in advance, allowing corrective actions to be taken before the model is deployed to real-world scenarios.
Solution Approach 2:
The system introduces an intermediary testing process that uses synthetic data with parameter shifts as a mediator to detect artifact reliance. This intermediary testing layer acts as a bridge between training on synthetic data and deployment in real-world scenarios, enabling the detection of problematic artifact correlations without requiring extensive real-world testing.
2Device complexity
If conventional overfitting detection methods are used, then the detection process is simple, but the detection precision deteriorates because artifacts are present in both training and testing sets
Solution Approach 1:
The system changes parameters of the synthetic data by applying shifts to one or more parameters (such as lighting conditions, camera angles, or environmental factors) when creating the testing set. This parameter shifting creates a controlled difference between training and testing synthetic data, enabling the detection of artifact reliance while maintaining the synthetic nature of both datasets.
3Measurement precision
If real-world data is used for testing to detect artifact reliance, then the measurement precision is improved, but the cost and time consumption increase
Solution Approach 1:
The system creates a synthetic copy of the real-world testing scenario by generating synthetic data with shifted parameters that mimics real-world conditions. This synthetic copy allows for efficient artifact reliance detection without requiring actual real-world data collection, thereby reducing time and cost while maintaining adequate detection precision.
Data Source
AI summary
In various examples, sets of testing data may be selected and applied to an MLM such that differences in performance of the MLM in the testing between the sets indicates and may be used to determine whether and/or an extent by which the MLM is trained to rely on artifacts. Training data for the MLM may be generated using a first value of a parameter that defines a value of a characteristic of the training data. For testing, first testing data may be selected that corresponds to a second value of the parameter that shifts the value in a first direction and second testing data may be selected that corresponds to a third value of the parameter that shifts the value in a second direction (e.g., opposite the first direction). Various possible actions may be taken based on results of analyzing the differences in performance.