Pre-activation Data for Out-of-Distribution Detection
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Solution Overview
Problem
Machine learning models face challenges in detecting out-of-distribution data samples, which can lead to reduced accuracy and delayed detection of model drift or adversarial attacks, due to the complexity of adding auxiliary models and the time required to identify significant changes in performance metrics.
Innovation Solution
The approach involves using pre-activation data from the hidden layers of the main model as input to a second machine learning model for ODS detection, optimizing feature extraction, and implementing techniques to reduce complexity and enable quick response to model drift or attacks, allowing for flexible deployment modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an auxiliary model is added to detect out-of-distribution samples, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent makes the main machine learning model perform multiple functions: both its original prediction task and ODS detection. By utilizing the pre-activation data from hidden layers for both purposes, the system eliminates the need for a separate auxiliary model, thereby maintaining detection capability while reducing overall system complexity
Solution Approach 2:
The main model serves itself by using its own internal pre-activation data to detect out-of-distribution samples. This self-service approach eliminates dependency on external auxiliary models, reducing system complexity while maintaining autonomous ODS detection capability
2Measurement precision
If performance metrics are monitored over large amounts of input data, then detection accuracy is improved, but detection time increases
Solution Approach 1:
The patent extracts pre-activation data from the hidden layers of the main model, which contains discriminative information about input distribution. This extraction approach enables detection based on intermediate representations rather than requiring analysis of large volumes of final performance metrics, thereby improving detection speed while maintaining accuracy
Solution Approach 2:
The system performs preliminary analysis by examining pre-activation data from hidden layers before final predictions are made. This early detection approach using intermediate representations allows for faster identification of ODS without waiting for accumulated performance metrics, reducing detection time while preserving accuracy
Data Source
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AI summary
Systems, methods, and software can be used to detect distribution assessments of production data sample of a machine learning operation. In some aspects, a method includes: receiving, from a first machine learning model, pre-activation data, wherein the pre-activation data comprises pre-activation information of one or more neurons of the first machine learning model, and the pre-activation data is obtained when the first machine learning model processes a production data sample to generate a prediction outcome; using, a second machine learning model to process the pre-activation data to generate a distribution assessment; and determining, based on the distribution assessment, wherein the production data sample is an adversarial data sample or a drift data sample.