arXiv:2601.11670v3 Announce Type: replace Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance. We propose CoVar, a confidence--variance framework that assesses pseudo-label reliability by jointly modeling Maximum Confidence (MC) and Residual-Class Variance (RCV). Starting from entropy minimization, we derive a second-order cross-entropy approximation showing that low-loss pseudo-labels are favored when MC is high and RCV is low, with a confidence-d
The proliferation of semi-supervised learning in AI development necessitates more robust and reliable methods for leveraging unlabelled data, addressing current limitations in pseudo-labeling techniques.
Improving pseudo-label selection directly enhances the efficiency and performance of AI models, particularly in data-scarce domains or when human labeling is prohibitively expensive, accelerating AI development cycles.
The introduction of CoVar provides a more reliable method for semi-supervised learning by jointly considering confidence and variance, potentially leading to more accurate and robust AI systems.
- · AI developers
- · Data scientists
- · Research institutions relying on semi-supervised learning
- · Industries with limited labeled data
- · Traditional fully supervised learning methods (relatively)
- · Inefficient pseudo-labeling techniques
Increased accuracy and efficiency in deploying AI models with less labeled data.
Faster development and iteration cycles for various AI applications, including agents and autonomous systems.
Potentially democratizes AI development by lowering the barrier to entry for regions or entities with fewer labeling resources.
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Read at arXiv cs.LG