Weakly Supervised Learning
Weakly supervised learning is a machine learning paradigm where models are trained using incomplete, inexact, or noisy labels, rather than fully annotated datasets. It addresses scenarios where obtaining large-scale, high-quality labeled data is expensive or impractical, by leveraging cheaper, weaker forms of supervision such as image tags, partial labels, or noisy annotations. This approach enables the development of models in domains like medical imaging, natural language processing, and computer vision with reduced labeling costs.
Developers should learn weakly supervised learning when working on projects with limited labeled data, high annotation costs, or noisy real-world datasets, such as in medical diagnosis, social media analysis, or autonomous driving. It is particularly useful for scaling machine learning applications where manual labeling is a bottleneck, allowing for efficient model training with imperfect supervision. This concept is essential for building robust AI systems in data-scarce or label-noisy environments.