Unbiased Pseudo Label

Unbiased pseudo-labeling aims to generate high-quality, representative synthetic labels for unlabeled data in semi-supervised learning, overcoming biases inherent in existing labeling methods or datasets. Current research focuses on developing techniques to mitigate these biases, employing methods like confidence score adjustments, Chebyshev-based constraints for label generation, and category-aware adaptive thresholding to create more reliable pseudo-labels for both classification and regression tasks. This work is crucial for improving the robustness and fairness of machine learning models, particularly in scenarios with limited labeled data or skewed data distributions, leading to more accurate and equitable outcomes across various applications.