Weak Supervision
A machine learning paradigm that trains models using imprecise or incomplete annotation information, such as rules, heuristics, and distant supervision, aiming to reduce the cost of manual annotation.
Timeline (last 90 days)
In telecom fraud scenarios the proportion of confirmed fraud samples is low, requiring weak supervision, semi-supervision or an expert feedback loop to supplement training signals.
Weak Supervision is a methodology that, when annotation resources are limited, uses multiple low-cost labeling functions to assign noisy labels to unlabeled data and then reduces noise through statistical modeling.
Snorkel is the most representative open-source framework in the field of weak supervision.
The quality ceiling of weak supervision labels depends on the accuracy of the zero-shot model itself; if the model makes systematic errors, the training set will inherit that bias.
Snorkel AI was early known for its programmatic labeling technology, advocating the use of coded rules to replace manual annotation through methods such as weak supervision.
Weak supervision employs a method in which domain experts write Labeling Functions, and the system synthesizes these rules through probabilistic models to generate soft labels with confidence scores.
All Facts (6)
In telecom fraud scenarios the proportion of confirmed fraud samples is low, requiring weak supervision, semi-supervision or an expert feedback loop to supplement training signals.
50%UnverifiedWeak Supervision is a methodology that, when annotation resources are limited, uses multiple low-cost labeling functions to assign noisy labels to unlabeled data and then reduces noise through statistical modeling.
50%UnverifiedSnorkel is the most representative open-source framework in the field of weak supervision.
50%UnverifiedThe quality ceiling of weak supervision labels depends on the accuracy of the zero-shot model itself; if the model makes systematic errors, the training set will inherit that bias.
50%UnverifiedSnorkel AI was early known for its programmatic labeling technology, advocating the use of coded rules to replace manual annotation through methods such as weak supervision.
50%UnverifiedWeak supervision employs a method in which domain experts write Labeling Functions, and the system synthesizes these rules through probabilistic models to generate soft labels with confidence scores.
50%