Active Learning Uncertainty via Root Mean Square Autocorrelation
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Solution Overview
Problem
Deep neural networks often provide inadequate uncertainty estimates, particularly for samples outside the training set, leading to overconfidence in predictions, which is a concern for safety-critical applications like autonomous driving and medical imaging.
Innovation Solution
A computer-implemented method and device for active machine learning that uses a root mean square autocorrelation of feature representations across multiple layers of a model to assess the reliability of predictions, selecting unannotated data based on a threshold value and iteratively training the model to improve classification accuracy and reduce uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for classification, then prediction speed and accuracy on training data are improved, but reliability of uncertainty estimates deteriorates (overconfidence on out-of-distribution samples)
Solution Approach 1:
The patent segments the feature space by computing autocorrelation separately for different feature dimensions and layers of the neural network. This segmentation allows the system to identify specific regions where out-of-distribution samples differ from training data, enabling more reliable uncertainty estimation by analyzing local feature patterns rather than relying on global confidence scores alone.
Solution Approach 2:
The patent introduces autocorrelation analysis as an intermediary mechanism between the neural network's prediction output and the uncertainty estimate. By computing autocorrelation of feature representations across layers, the system creates an intermediate measure that captures the consistency of feature patterns, which then informs the final uncertainty assessment and corrects overconfidence issues.
2Measurement precision
If more annotated data are collected for training, then model accuracy is improved, but data acquisition cost and time increase
Solution Approach 1:
The patent performs preliminary autocorrelation analysis on unannotated data to identify samples with high uncertainty or out-of-distribution characteristics before they are added to the training set. This preliminary assessment allows the system to prioritize annotation efforts on the most valuable samples, reducing the overall time and resources needed for data collection while maintaining model improvement efficiency.
Solution Approach 2:
The patent implements a feedback loop where the autocorrelation-based uncertainty estimates from the current model are used to guide the selection of samples for annotation and retraining. This feedback mechanism ensures that each iteration of data collection focuses on samples that will most improve model performance, optimizing the trade-off between accuracy gains and data acquisition costs.
3Reliability
If autocorrelation analysis is performed across multiple layers, then uncertainty estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts autocorrelation features from specific critical layers of the neural network rather than analyzing all layers equally. By identifying and focusing on layers that provide the most informative feature representations for uncertainty estimation, the system reduces computational overhead while maintaining or improving uncertainty estimation accuracy compared to analyzing the entire network depth.
Data Source
AI summary
A device and a computer-implemented method for data-efficient active machine learning. Annotated data are provided. A model is trained for a classification of the data as a function of the annotated data. For unannotated data, values of an acquisition function of the unannotated data are determined, and the unannotated data for the active machine learning whose values for the acquisition function satisfy a criterion are acquired from the unannotated data. An autocorrelation is determined via a feature representation for a sample from the unannotated data to be assessed, in particular from at least one layer of the model. The value of the acquisition function of this sample is determined as a function of a root mean square via the autocorrelation, in particular in at least one dimension.