Editing Semi-Supervised Learning (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a semi-supervised pipeline: (1) Start with self-training — simple, effective, easy to implement. (2) Set high confidence threshold (0.95+) to avoid noisy pseudo-labels. (3) Apply curriculum: increase unlabeled data usage as model improves (FlexMatch adaptive threshold). (4) For vision: use FixMatch with RandAugment strong augmentation. (5) For NLP: leverage domain-adaptive pre-training on unlabeled data, then fine-tune on labels. (6) Monitor pseudo-label quality: compute accuracy of pseudo-labels on held-out labeled data as a proxy for noise level.

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Semi-Supervised Learning]]
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