ScholarWorks@동국대학교

초록

This study addresses the limitations of existing dynamic pseudo-labeling (DPL) techniques, which often utilize static or dynamic thresholds for confident sample selection. The existing methods fail to capture the non-linear relationship between task accuracy and model confidence, particularly in the context of overconfidence. This can limit the model's learning opportunities for high entropy samples that significantly influence a model's generalization ability. To solve this, we propose a novel gradient pass-based DPL technique that incorporates the high-entropy samples, which are typically overlooked. Our approach introduces two classifiers-low gradient pass (LGP) and high gradient pass (HGP)-to derive over- and under-confident dynamic thresholds that indicate the class-wise overconfidence acceleration, respectively. By combining the under- and overconfident states from the GP classifiers, we create a more adaptive and accurate PL method. Our main contributions highlight the importance of considering both low and high-confidence samples in enhancing the model's robustness and generalization for improved PL performance. © 2025 Elsevier B.V., All rights reserved.

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