ML Classification Routing for Out-of-Distribution Inputs
Find Innovative SolutionsGenerate Solutions
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 input data, 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, the system either uses the first classification or defers to an expert for classification when the input is deemed out-of-distribution.
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 follows the training distribution, and only passes in-distribution data to the classifier. This intermediary layer prevents out-of-distribution data from reaching the classifier, thereby improving classification reliability while maintaining a relatively simple overall structure.
2Measurement precision
If the system uses additional likelihood calculations and expert verification, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial verification by performing distribution detection on all inputs but only invoking expert verification or additional likelihood calculations when the distribution detection indicates out-of-distribution data. For in-distribution data, the system uses the standard classification pipeline, thus avoiding unnecessary processing time while maintaining high accuracy for critical cases.
Solution Approach 2:
The distribution detection is performed as a preliminary action before the main classification process. By quickly assessing whether input data belongs to the training distribution beforehand, the system can route in-distribution data through the fast standard classification pipeline while reserving additional verification resources only for out-of-distribution cases, thereby minimizing overall processing time while maintaining accuracy.
3Reliability
If the model rejects out-of-distribution data, then misclassification risk decreases, but system productivity decreases due to rejected inputs
Solution Approach 1:
The distribution detection module serves as an intermediary that filters out-of-distribution data before it reaches the classification module. This allows the system to maintain high reliability by preventing misclassification of out-of-distribution data, while the filtered in-distribution data continues to be processed efficiently, preserving overall system productivity.
Solution Approach 2:
The system applies different quality standards to different types of input data: in-distribution data is processed through the standard efficient pipeline, while out-of-distribution data is either rejected or routed to specialized handling. This local quality approach ensures high reliability for the majority of in-distribution data while maintaining productivity by not applying stringent checks to all inputs uniformly.
Data Source
Figure 1~2
Figure 3
AI summary
A device and a computer-implemented method for machine learning, wherein the method comprises providing an input (101) for a model (200), determining with the model (200) a first classification that indicates a class for the input (101), determining with the model (200) depending on the input (101) a likelihood that an expert determines a correct classification for the input (101), determining with the model (200) depending on the input (101) a likelihood that the input (101) is in-distribution data or out-of-distribution data with respect to a distribution of data that the model (200) is trained on, determining a second classification that indicates whether the input (101) is considered as in-distribution data or out-of-distribution data with respect to the distribution of data that the model (200) is trained on depending on the first classification and depending on the likelihoods, determining an output (105) of the model (200) depending on the first classification and the second classification, and outputting the output (105).