Modified GAN Drift Detection Using Hidden Layer Metrics
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Solution Overview
Problem
ML models often require a large amount of training data and are prone to deterioration due to model drift, making them less robust over time as data evolves, especially with sparse datasets.
Innovation Solution
A modified GAN architecture is used to identify and quantify ML model drift by analyzing hidden layer outputs through a latent vector analyzer, determining metrics such as average confidence and variance per class, to determine if further training is needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a ML model is trained with a sparse dataset, then training time and data requirements are reduced, but model accuracy and robustness deteriorate
Solution Approach 1:
The patent uses a generative adversarial network (GAN) to create synthetic training data that copies the statistical properties and patterns of real data. The GAN generates artificial samples that augment the sparse dataset, enabling the ML model to be trained on sufficient data without requiring extensive real-world data collection, thus maintaining model robustness while reducing actual data requirements
Solution Approach 2:
The system performs preliminary drift detection and data generation before actual model training or deployment. By using the GAN to pre-generate synthetic data that anticipates potential drift scenarios, the system prepares the model in advance for various data distributions, improving robustness without requiring extensive real-time data during operation
2Ease of operation
If a ML model is deployed without continuous monitoring, then operational simplicity is maintained, but model drift goes undetected and performance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the trained ML model continuously monitors incoming data for drift conditions. The system compares current data distributions against the training distribution, detects deviations, and triggers retraining or alerts when drift is identified. This automated feedback loop maintains model performance without requiring constant manual intervention, balancing operational simplicity with reliability
Solution Approach 2:
The system enables the ML model to self-monitor and self-diagnose drift conditions using the deployed model itself to analyze incoming data patterns. The model performs self-assessment of its own performance degradation and automatically identifies when retraining is needed, reducing the need for external monitoring infrastructure and maintaining operational simplicity while ensuring reliability
3Reliability
If continuous monitoring for model drift is implemented, then model robustness is maintained, but computational resources and system complexity increase
Solution Approach 1:
The patent makes the deployed ML model multi-functional by using it both for its primary classification task and for drift detection. The same model weights and architecture are leveraged to analyze both input data for classification and statistical properties for drift monitoring. This universal usage eliminates the need for separate monitoring models or complex monitoring infrastructure, maintaining robustness while minimizing system complexity
Solution Approach 2:
The system uses the deployed model's own internal representations and decision boundaries to detect drift, rather than requiring external monitoring systems. The model self-assesses whether incoming data matches its training distribution by analyzing its own activation patterns and prediction confidence, enabling robust monitoring with minimal additional computational overhead and system complexity
Data Source
AI summary
Discussed herein are devices, systems, and methods for machine learning (ML) model drift detection. A method can include receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model, operating the deployed ML model in a modified generative adversarial network (GAN) architecture, while operating the deployed ML model, recording output of a hidden layer of the deployed ML model, determining a metric of the output, and re-deploying the deployed ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.