Neural Network Accuracy Estimation Under Distribution Shift
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models face challenges in estimating their accuracy when operating on out-of-distribution data, as traditional methods rely on labeled data which is rare and expensive, and fail to generalize well to new environments.
Innovation Solution
A method that estimates the accuracy of a neural network on out-of-distribution data by determining the in-distribution agreement between multiple models, comparing it with the out-of-distribution agreement, and using this correlation to estimate the out-of-distribution accuracy without requiring labeled out-of-distribution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional accuracy estimation methods using labeled data are used, then measurement precision is improved, but loss of substance increases due to the expensive and rare labeled out-of-distribution data
Solution Approach 1:
The patent creates virtual labeled out-of-distribution data by copying and adapting in-distribution labeled data through distribution shift simulation. Multiple virtual labeled datasets are generated by applying various distribution shift transformations to the original in-distribution data, enabling accurate model evaluation without requiring actual labeled out-of-distribution data
Solution Approach 2:
The patent performs preliminary actions by pre-training multiple models on in-distribution data and pre-generating virtual labeled out-of-distribution datasets before the actual evaluation phase. This preliminary preparation enables subsequent accurate accuracy estimation while avoiding the need to collect and label real out-of-distribution data
2Reliability
If multiple machine learning models are trained and evaluated to improve reliability, then device complexity increases, but this enables better accuracy estimation under distribution shift
Solution Approach 1:
The patent merges multiple model evaluation results into a unified accuracy estimation framework. By combining predictions from multiple models trained on in-distribution data and evaluating their performance on virtual out-of-distribution datasets, the system achieves reliable accuracy estimation while managing complexity through integrated processing
Solution Approach 2:
The patent introduces virtual labeled out-of-distribution data as an intermediary between in-distribution training data and actual out-of-distribution evaluation. This intermediary enables reliable accuracy estimation by bridging the gap between training and deployment distributions without requiring direct access to real out-of-distribution labeled data
Data Source
AI summary
Methods and systems of estimating an accuracy of a neural network on out-of-distribution data. In-distribution accuracies of a plurality of machine learning models trained with in-distribution data are determined. The plurality of machine learning models includes a first model, and a remainder of models. In-distribution agreement is determined between (i) an output of the first machine learning model executed with an in-distribution dataset and (ii) outputs of a remainder of the plurality of machine learning models executed with the in-distribution dataset. The machine learning models are also executed with an unlabeled out-of-distribution dataset, and an out-of-distribution agreement is determined. The in-distribution agreement is compared with the out-of-distribution agreement. Based on a result of the comparison being within a threshold, an accuracy of the first machine learning model on the unlabeled out-of-distribution dataset is estimated based on (i) the in-distribution accuracies, (ii) the in-distribution agreement, and (iii) the out-of-distribution agreement.