ConceptSemi-supervised Learning
Semi-Supervised Learning
A paradigm between supervised and unsupervised learning that trains models using a small amount of labeled data together with a large amount of unlabeled data. It is suited to scenarios where labeling is costly.
Timeline (last 90 days)
Oct 13
Common methods in academia and industry for addressing few-shot defect detection include anomaly detection (such as autoencoder reconstruction error methods), data augmentation and synthetic defect generation, transfer learning, and semi-supervised/self-supervised learning.
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