Uncertainty-Guided Segmentation Training for Rare Feature Labels
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Solution Overview
Problem
Existing machine learning models for autonomous vehicles struggle with efficiently training on rare features due to insufficient data and high supervision requirements, leading to inaccurate classification of under-represented elements.
Innovation Solution
A dynamic training pipeline that selectively annotates and trains on uncertain data using a disagreement-based active learning approach, focusing on data that the model is unsure about, and employing a U-Net neural network for semantic segmentation to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning training is used with large datasets, then classification capability is improved, but training efficiency and resource consumption deteriorate
Solution Approach 1:
The system performs self-training by automatically identifying uncertain predictions from its own operation and using these to improve its classification capabilities without requiring external annotated datasets, thereby improving training efficiency while maintaining accuracy
Solution Approach 2:
The system implements a feedback loop where predictions with uncertainty above a threshold are identified, annotated with ground truth labels, and fed back into the training process to continuously improve the model's classification accuracy for rare features
2Measurement precision
If more training data is collected for rare features, then classification accuracy is improved, but data collection complexity and supervision requirements worsen
Solution Approach 1:
The system automatically identifies which predictions are uncertain and which features need improvement, eliminating the need for manual analysis and supervision to determine what data should be collected for training
Solution Approach 2:
The uncertainty threshold mechanism provides automatic feedback on which predictions need improvement, guiding the data collection process to focus only on uncertain cases rather than requiring comprehensive supervision of all data
3Measurement precision
If active learning with uncertainty sampling is implemented, then training data quality is improved, but computational complexity worsens
Solution Approach 1:
The system changes the parameter of interest from comprehensive model evaluation to simple uncertainty threshold comparison, reducing computational complexity while maintaining training data quality through efficient uncertainty sampling
Data Source
AI summary
A training system and method identifies pixels in an image that fall within a desired range of uncertainty and uses them to train a machine learning model. Predicted pixel classification probability values may be normalized. A count of normalized predicted pixel classification probability values that fall within the range of uncertainty may be obtained for each classification label. For each classification label, the count may be divided by the total number of normalized predicted pixel classification probability values to generate an associated uncertainty ratio. Pixels of an under-represented classification label that has an uncertainty ratio in the desired range of uncertainty may be used to train the machine learning model.