arXiv:2407.05370v3 Announce Type: replace Abstract: Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data. In such scenarios, the generated pseudo-labels tend to exhibit a bias toward the majority class, and models relying on these pseudo-labels can further amplify this bias. Existing imbalanced SSL algorithms explore pseudo-labeling strategies based on either pseudo-label refinement (PLR) or threshold adjustment (THA), aiming to mitigate the bias through heuristic-driven designs. However, through a careful statistical analysis, we find that e
The paper addresses a significant challenge in semi-supervised learning (SSL) for AI, which is becoming increasingly relevant as the demand for efficient model training with limited labeled data grows.
Improved semi-supervised learning techniques can lead to more robust and less resource-intensive AI development, impacting various applications from autonomous systems to data analysis.
This research offers a method to mitigate bias in pseudo-labeling for imbalanced datasets, potentially making SSL more reliable and broadly applicable in real-world scenarios.
- · AI developers
- · Machine learning researchers
- · Sectors with imbalanced datasets (e.g., medical imaging, fraud detection)
- · Inefficient imbalanced SSL algorithms
AI models trained with imbalanced data will become more accurate and fair.
This could accelerate the deployment of AI in domains where data imbalance is a common challenge, reducing the need for extensive manual labeling.
More robust and efficient AI training could lower the barrier to entry for developing complex AI systems, fostering innovation across various industries.
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