Weak Supervision Learning Path

Motivation: limited labeled data costs

Limited labeled data is a major motivation for weak supervision / semi-supervised learning. Acquiring accurate labels often requires skilled human effort (e.g., transcription) or expensive physical experiments (e.g., determining protein structure), making large fully labeled datasets impractical. In contrast, collecting unlabeled data is usually much cheaper and easier. Semi-supervised learning addresses this by training models using a small set of human-labeled examples together with a much larger set of unlabeled (or imprecisely labeled) examples. The goal is to achieve better performance than purely supervised learning (which discards unlabeled data) or purely unsupervised learning (which discards labels). This idea is especially valuable in domains like predictive maintenance, where failures are rare and high-quality labeled fault data is scarce; weak supervision leverages imperfect supervision sources (e.g., noisy labels, heuristics, expert rules, partially labeled datasets) to build robust predictive models with reduced reliance on costly labels.

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Core definition of weak supervision

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