Trigger Classifier for Edge-Case Training Data Collection
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Solution Overview
Problem
Deep learning systems, such as those used for autonomous driving, face limitations in performance due to the quality and quantity of training data, particularly for complex scenarios, requiring significant resources and effort to collect and annotate relevant examples.
Innovation Solution
A system and method that rapidly generates training data by leveraging vehicles equipped with sensors to collect and transmit image features or objects of interest, using classifiers to identify and flag relevant data, which is then used to improve the performance of machine learning models through continuous updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If significant resources are invested in collecting, curating, and annotating training data, then the quality and quantity of training data improves, but the time and effort required increases significantly
Solution Approach 1:
The system enables vehicles to automatically collect and transmit training data during normal operations without requiring manual intervention. The trigger classifier automatically identifies edge cases and flags relevant data for collection, eliminating the need for human curators to manually search for and annotate training examples.
Solution Approach 2:
The system uses a trigger classifier that provides feedback to identify when edge cases are detected, automatically initiating data collection and transmission processes. This feedback mechanism enables continuous improvement of the training dataset based on real-world performance gaps.
2Measurement precision
If manual collection and annotation methods are used, then data quality can be ensured, but the process becomes tedious and resource-intensive
Solution Approach 1:
The trigger classifier acts as an intermediary between the deep learning system and the training data collection process. It automatically identifies edge cases and filters relevant data, replacing manual annotation processes with an automated classification mechanism that maintains quality while reducing complexity.
Solution Approach 2:
The system replaces manual mechanical annotation processes with an automated computational classification system. The trigger classifier uses computational algorithms to identify and flag relevant training data, substituting human labor with automated machine learning processes.
3Reliability
If data is collected for particular use cases, then the model can be improved on specific tasks, but it is difficult to collect sufficient data for rare edge cases
Solution Approach 1:
The trigger classifier provides feedback by monitoring the deep learning system's performance and automatically identifying when edge cases are encountered. This feedback mechanism ensures that rare but important examples are captured and added to the training dataset, improving model reliability on specific use cases without requiring manual data collection efforts.
Data Source
AI summary
Systems and methods for obtaining training data are described. An example method includes receiving sensor and applying a neural network to the sensor data. A trigger classifier is applied to an intermediate result of the neural network to determine a classifier score for the sensor data. Based at least in part on the classifier score, a determination is made whether to transmit via a computer network at least a portion of the sensor data. Upon a positive determination, the sensor data is transmitted and used to generate training data.