Neural Network Object Sample Selection for Class Imbalance
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Solution Overview
Problem
Neural networks used for object detection face performance issues due to class imbalance in training data, where variations in the number of object samples from different classes lead to poor performance, especially for classes with fewer samples.
Innovation Solution
A method for selecting object samples from multiple datasets by determining an importance score for each sample, defining thresholds, and balancing the number of samples across classes to ensure equal representation while maintaining high quality and relevance, thereby alleviating class imbalance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If object samples are selected from multiple datasets with different properties, then the training data diversity is improved, but class imbalance occurs causing poor performance for classes with fewer samples
Solution Approach 1:
The patent changes the parameter of sample selection by introducing importance scores and thresholds. Instead of uniformly selecting samples, it dynamically adjusts which samples are selected based on their importance scores, ensuring that samples from underrepresented classes with high importance scores are included while maintaining balance across classes.
Solution Approach 2:
The patent implements a feedback mechanism by calculating the variation of sample numbers across classes and using this information to adjust the selection process. The method iteratively selects samples and monitors class balance, ensuring that the final training set achieves both diversity and balanced representation.
2Reliability
If the number of object samples from different classes is balanced, then the performance for classes with fewer samples is improved, but the quality and relevance of selected samples may be compromised
Solution Approach 1:
The patent introduces importance scores as a new parameter to evaluate sample quality and relevance. By setting importance score thresholds, the method ensures that only samples meeting minimum quality standards are selected, while still achieving class balance through the scoring mechanism.
3Measurement precision
If importance score thresholds are set high to ensure sample quality, then the relevance of selected samples is improved, but the number of available samples for balancing decreases
Solution Approach 1:
The patent uses feedback from the variation calculation to adjust the selection process. By monitoring the balance across classes, the method can identify when additional samples are needed and selectively include samples with importance scores at or near the threshold, thereby maintaining quality while achieving sufficient quantity for balance.
Data Source
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AI summary
The present invention generally relates to method for selecting object samples (208) for training of a neural network (200) from more than one dataset (202, 204, 206) comprising annotated object samples of at least two object classes ("Class A-D"), the method comprising: determining (S102) an importance score for at least a portion of the annotated object samples; defining (S104) a set of importance score thresholds (210); and selecting (S106) a number of annotated object samples (208) from each object class that fulfill a respective importance score threshold, and that provides the smallest variation of the number of object samples between the object classes, to be used for training of the neural network (200).