Multi-Perspective Object Classification Using Pseudo-Labels
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Solution Overview
Problem
Existing neural networks for object detection in vehicle environments require extensive manual labeling of training examples from multiple perspectives and modalities, which is costly and time-consuming.
Innovation Solution
Iteratively generate pseudo-labels by optimizing neural networks using a cost function that evaluates similarity and consistency of intermediate products from unlabeled examples, allowing them to self-label and increase the labeled training examples over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for all training examples from multiple perspectives and modalities, then classification accuracy is improved, but labeling effort and time consumption increase significantly
Solution Approach 1:
The system performs self-labeling by using the neural network to automatically generate labels for unlabeled training examples. The network processes unlabeled examples, generates intermediate products, and uses consistency checks to create pseudo-labels without human intervention, allowing the system to service itself in the labeling process
Solution Approach 2:
The method changes the state of training examples from unlabeled to labeled by generating pseudo-labels. It iteratively transforms the parameter state of training data, converting unlabeled examples into labeled ones through the neural network's intermediate product consistency evaluation, thereby expanding the labeled dataset without proportional increase in manual effort
2Reliability
If more training examples are labeled to improve network performance, then classification reliability is improved, but cost and complexity increase
Solution Approach 1:
The neural network system performs self-evaluation and self-labeling by automatically assessing the consistency of its own intermediate products. The system services itself by generating pseudo-labels through internal consistency checks, eliminating the need for external manual labeling processes and reducing overall system complexity
Solution Approach 2:
The method implements feedback loops where the neural network's intermediate products are evaluated for consistency, and this feedback is used to generate pseudo-labels that are then fed back into the training process. This iterative feedback mechanism improves reliability while maintaining manageable complexity through automated processes
3Manufacturing precision
If manual labeling is used for diverse perspectives and modalities, then training data quality is improved, but productivity decreases
Solution Approach 1:
The system automatically generates high-quality pseudo-labels for diverse perspectives and modalities without manual intervention. The neural network services itself by processing unlabeled examples across different viewpoints and sensor types, generating consistent intermediate products that serve as reliable training labels
Solution Approach 2:
The method performs preliminary processing of unlabeled training examples by generating intermediate products and evaluating their consistency before final label assignment. This preliminary action ensures high training data quality while maintaining productivity, as the automated process handles diverse data types without manual bottlenecks
Data Source
AI summary
A method for training one or more neural networks for processing measurement data includes providing training examples for the measurement data including both training examples labeled with target classification scores and unlabeled training examples, and processing the training examples by the one or more neural networks into classification scores. The method further includes, with respect to the labeled training examples, using a specified cost function to evaluate to what extent (i) the classification scores correspond to the respective target classification scores, and (ii) intermediate products formed from similar training examples are similar to each other while intermediate products formed from dissimilar training examples are dissimilar to each other. The method further includes optimizing parameters characterizing a behavior of the one or more neural networks with the goal that an assessment by the cost function is expected to improve during further processing of the training examples.