Machine Learning Classification With Out-of-Distribution Detection
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Solution Overview
Problem
Machine learning systems face challenges in providing reliable classifications when encountering input data that shifts from the training distribution, leading to potential misclassifications and unreliable outputs.
Innovation Solution
A method for machine learning that involves determining a first classification for an input, calculating the likelihood of correct classification by an expert, and assessing whether the input is in-distribution or out-of-distribution data. Based on these determinations, a second classification is made to decide whether to use the first classification or defer to an expert, thereby ensuring reliable outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the model processes all input data without distribution checking, then processing speed is maintained, but classification reliability deteriorates due to out-of-distribution data
Solution Approach 1:
The system segments the classification process into two independent modules: a distribution detection module that determines whether input data belongs to the training distribution, and a classification module that performs actual classification. This segmentation allows the system to handle out-of-distribution data separately, improving reliability without requiring complete system redesign.
Solution Approach 2:
The distribution detection module acts as an intermediary between the input data and the classification module. It first assesses whether the input data conforms to the training distribution and only passes conforming data to the classification module, thereby preventing unreliable classifications while maintaining overall system efficiency.
2Reliability
If the system uses additional likelihood calculations and expert determinations, then output reliability improves, but processing time increases
Solution Approach 1:
The system performs distribution detection as a preliminary action before actual classification. By checking whether input data conforms to the training distribution first, the system avoids wasting processing time on out-of-distribution data that would produce unreliable classifications, thus reducing overall processing time while maintaining reliability for valid inputs.
Solution Approach 2:
The system applies full reliability checks (distribution detection + likelihood calculation + expert determination) only when necessary, i.e., when the distribution detector indicates in-distribution data. For out-of-distribution data, the system takes partial action by immediately rejecting classification without performing additional time-consuming checks, thereby optimizing processing time.
Data Source
AI summary
A device and a computer-implemented method for machine learning. The method includes: providing an input for a model, determining with the model a first classification that indicates a class for the input, determining with the model depending on the input a likelihood that an expert determines a correct classification for the input, determining with the model depending on the input a likelihood that the input is in-distribution data or out-of-distribution data with respect to a distribution of data that the model is trained on, determining a second classification that indicates whether the input is considered as in-distribution data or out-of-distribution data with respect to the distribution of data that the model is trained on depending on the first classification and depending on the likelihoods, determining an output of the model depending on the first classification and the second classification, and outputting the output.