Difference-Signal Classifier Training for Automated Label Generation
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Solution Overview
Problem
Supervised training of classifiers requires a significant amount of labeled data, which is cumbersome and time-consuming to obtain manually, especially in safety-critical environments like autonomous vehicles, necessitating a more efficient and automated labeling method.
Innovation Solution
A method using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to generate difference signals for automatic labeling, enabling unsupervised training and testing of classifiers by generating bounding boxes and semantic segmentations without human supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to create training datasets, then labeling accuracy can be ensured, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system uses automated difference signal computation and threshold-based labeling to enable the dataset creation process to serve itself without human intervention. The generator automatically creates labeled training data by computing differences between input and generated images, eliminating the need for manual annotation while maintaining consistent labeling standards through algorithmic thresholding.
2Reliability
If more labeled data is collected to improve classifier performance, then classification accuracy improves, but the manual labeling process becomes even more time-consuming
Solution Approach 1:
The patent replaces the mechanical process of manual labeling with an automated computational system. The difference signal computation mechanism automatically generates labels by comparing input images with generator outputs, substituting human annotators with an algorithmic process that can handle large volumes of data efficiently while maintaining labeling consistency.
3Reliability
If manual labeling is used for safety-critical environments, then label reliability is ensured, but the complexity and cost of data preparation increases
Solution Approach 1:
The difference signal acts as an intermediary that bridges input images and labeled training data. By computing the difference between original images and generator outputs, the system creates an automatic labeling mechanism that maintains reliability through consistent algorithmic processing while reducing the complexity of manual annotation workflows.
Data Source
AI summary
A computer-implemented method trainings a classifier. The classifier is configured to provide a classifier output signal characterizing a classification of a first input signal. The classifier is trained based on a training dataset. The method includes generating a training output signal with features of a second class based on a second input signal with features of a first class using a first generator, or generating a mask signal indicating which parts of the second input signal show the features of the first class using the first generator; and generating the training output signal or the mask signal based on the second input signal using the first generator. The method further includes generating a difference signal corresponding to the second input signal, the difference signal based on a difference between the second input signal and the training output signal, or the difference signal is the mask signal.