WeaNF: Weak Supervision with Normalizing Flows

04/28/2022
by   Andreas Stephan, et al.
3

A popular approach to decrease the need for costly manual annotation of large data sets is weak supervision, which introduces problems of noisy labels, coverage and bias. Methods for overcoming these problems have either relied on discriminative models, trained with cost functions specific to weak supervision, and more recently, generative models, trying to model the output of the automatic annotation process. In this work, we explore a novel direction of generative modeling for weak supervision: Instead of modeling the output of the annotation process (the labeling function matches), we generatively model the input-side data distributions (the feature space) covered by labeling functions. Specifically, we estimate a density for each weak labeling source, or labeling function, by using normalizing flows. An integral part of our method is the flow-based modeling of multiple simultaneously matching labeling functions, and therefore phenomena such as labeling function overlap and correlations are captured. We analyze the effectiveness and modeling capabilities on various commonly used weak supervision data sets, and show that weakly supervised normalizing flows compare favorably to standard weak supervision baselines.

READ FULL TEXT
research
04/14/2022

ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision

A way to overcome expensive and time-consuming manual data labeling is w...
research
09/15/2020

Constrained Labeling for Weakly Supervised Learning

Curation of large fully supervised datasets has become one of the major ...
research
11/30/2021

Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis

Obtaining annotations for large training sets is expensive, especially i...
research
10/25/2022

SepLL: Separating Latent Class Labels from Weak Supervision Noise

In the weakly supervised learning paradigm, labeling functions automatic...
research
04/13/2022

Label Augmentation with Reinforced Labeling for Weak Supervision

Weak supervision (WS) is an alternative to the traditional supervised le...
research
06/03/2022

XPASC: Measuring Generalization in Weak Supervision by Explainability and Association

Weak supervision is leveraged in a wide range of domains and tasks due t...
research
10/06/2022

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm...

Please sign up or login with your details

Forgot password? Click here to reset