ML Classification Uncertainty for Distribution Shift Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep learning models, such as convolutional neural networks, are not robust to variations in scanning sites and subject populations, leading to sub-optimal performance on test data from distributions different from the training data, with no immediate indication of sub-optimal performance in real-life scenarios.
Innovation Solution
A method to quantify model classification uncertainty by training a convolutional neural network using a first data distribution, determining classifications for a second disjoint data distribution, and evaluating uncertainty through node weight dropout and variational autoencoders to assess the difference between distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning model is trained on a specific data distribution, then it achieves good classification performance on that distribution, but it shows sub-optimal performance on test data from different distributions with no immediate indication of the performance degradation
Solution Approach 1:
The patent applies preliminary action by training an auxiliary classifier alongside the main model during the training phase. This auxiliary classifier is specifically designed to detect whether input data belongs to the training distribution or a different distribution. By preparing this detection mechanism in advance, the system can identify distribution shifts before they cause unreliable predictions, allowing for appropriate handling or rejection of out-of-distribution samples.
Solution Approach 2:
The patent introduces an intermediary component - the auxiliary classifier - that acts as a mediator between the input data and the main classification model. This intermediary first assesses whether the input data conforms to the training distribution, and only then does the main model process the data. This intermediary layer protects the main model from making unreliable predictions on out-of-distribution data while maintaining high accuracy on in-distribution data.
2Adaptability or versatility
If a model is trained to deliver consistent results across different scanning sites, then it improves generalization, but it requires training data from multiple distributions which increases data requirements and complexity
Solution Approach 1:
The patent applies segmentation by separating the classification task into two distinct components: the main classification model that performs the primary classification, and an auxiliary classifier that specifically detects distribution characteristics. This segmentation allows the system to handle different data distributions more effectively without requiring extensive training data from each distribution, as the auxiliary classifier learns distribution patterns independently.
Solution Approach 2:
The auxiliary classifier serves multiple functions: it identifies out-of-distribution samples, provides uncertainty quantification, and can guide the main model's processing. This multi-functional component enables the system to generalize across different scanning sites and populations without requiring separate models for each distribution, reducing overall system complexity and data requirements.
Data Source
AI summary
Evaluating machine learning model classifications, training a machine learning classification model using a training data set from a first data distribution, determining a classification for test data from a second data distribution using the machine learning classification model, wherein the first data distribution and the second data distribution are disjoint distributions, determining an uncertainty for the classification of the test data according to a difference between the first data distribution and the second data distribution.