Leveling the Playing Field: Boosting Rare Classes with Confidence-Guided Learning
![The weighting function [latex]\Omega(p_t, f_c)[/latex] demonstrates an asymmetric relationship between prediction confidence [latex]p_t[/latex] and class frequency [latex]f_c[/latex], utilizing a pivotal point ω to differentiate low-confidence regions and facilitate smooth transitions between patterns characteristic of both tail and head classes.](https://arxiv.org/html/2601.15924v1/wireframe.png)
A new approach dynamically adjusts training to prioritize challenging examples from underrepresented classes, improving performance on imbalanced datasets.
![The weighting function [latex]\Omega(p_t, f_c)[/latex] demonstrates an asymmetric relationship between prediction confidence [latex]p_t[/latex] and class frequency [latex]f_c[/latex], utilizing a pivotal point ω to differentiate low-confidence regions and facilitate smooth transitions between patterns characteristic of both tail and head classes.](https://arxiv.org/html/2601.15924v1/wireframe.png)
A new approach dynamically adjusts training to prioritize challenging examples from underrepresented classes, improving performance on imbalanced datasets.

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