Mini-Batch Learning for Semantic Segmentation Rare Classes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mini-batch learning methods for semantic segmentation result in low rare class determination accuracy due to class biases in the learning input and annotation images, leading to inadequate learning of rare classes.
Innovation Solution
A mini-batch learning apparatus that calculates the area ratio of classes in the data, specifies rare classes, and adjusts the update level of the machine learning model by reducing the learning coefficient for rare classes to mitigate class bias, thereby improving overall class determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mini-batch learning is performed using conventional methods, then learning efficiency is improved, but rare class determination accuracy deteriorates due to class bias in the data
Solution Approach 1:
The patent applies local quality by differentiating the update process based on class rarity. The update unit performs different update operations depending on whether a class is identified as rare or common. For rare classes, the update operation is adjusted to prevent overwriting learned features, while common classes receive standard updates. This localized differentiation in update quality resolves the contradiction by maintaining high learning efficiency overall while specifically protecting rare class determination accuracy.
2Measurement precision
If the learning coefficient is reduced for rare classes, then rare class determination accuracy is improved, but overall learning speed deteriorates
Solution Approach 1:
The patent implements local quality by applying different learning coefficients to different classes based on their rarity. The update unit identifies rare classes and applies a reduced learning coefficient specifically to those classes, while maintaining the normal learning coefficient for common classes. This localized adjustment improves rare class determination accuracy without significantly impacting the overall learning speed, as the majority of common classes continue to learn at full speed.
3Ease of manufacture
If class bias in training data is not addressed, then training simplicity is maintained, but model fairness and accuracy for rare classes deteriorates
Solution Approach 1:
The patent implements feedback by introducing a feedback unit that monitors the determination accuracy for each class. The feedback unit provides information about rare classes back to the update unit, which then adjusts the update operation accordingly. This feedback mechanism maintains training simplicity by automatically identifying and handling rare classes without requiring manual intervention, while simultaneously improving model accuracy for rare classes through targeted update adjustments.
Data Source
Figure 1
Figure 2
Figure 3(A)~3(B)
AI summary
There are provided a mini-batch learning apparatus capable of preventing a decrease in a class determination accuracy of a machine learning model for performing semantic segmentation, an operation program of the mini-batch learning apparatus, an operation method of the mini-batch learning apparatus, and an image processing apparatus. In a case where the operation program is started, a CPU of the mini-batch learning apparatus functions as a calculation unit, a specifying unit, and an update unit. The calculation unit calculates an area ratio of each of a plurality of classes in mini-batch data. The specifying unit specifies a rare class of which the area ratio is lower than a setting value. The update unit sets an update level of the machine learning model in a case where the rare class is specified by the specifying unit to be lower than an update level of the machine learning model in a case where the rare class is not specified by the specifying unit.