DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning

• URL: http://arxiv.org/abs/2411.08340v1
• Date: Wed, 13 Nov 2024 05:09:28 GMT
• Title: DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning
• Authors: Zhimin Chen, Bing Li,
• Abstract summary: Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation.
• Score: 4.259908158892314
• License:
• Abstract: Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from underrepresented classes. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation. Through extensive experiments in 3D SSL, our method surpasses state-of-the-art counterparts in classification and detection tasks, highlighting its effectiveness in tackling data imbalance. This approach presents a significant advancement in SSL for 3D datasets, providing a robust solution for data imbalance issues.

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