  Since manually labeling training data is slow and expensive, recent
industrial and scientific research efforts have turned to weaker or noisier
forms of supervision sources. However, existing weak supervision approaches
fail to model multi-resolution sources for sequential data, like video, that
can assign labels to individual elements or collections of elements in a
sequence. A key challenge in weak supervision is estimating the unknown
accuracies and correlations of these sources without using labeled data.
Multi-resolution sources exacerbate this challenge due to complex correlations
and sample complexity that scales in the length of the sequence. We propose
Dugong, the first framework to model multi-resolution weak supervision sources
with complex correlations to assign probabilistic labels to training data.
Theoretically, we prove that Dugong, under mild conditions, can uniquely
recover the unobserved accuracy and correlation parameters and use parameter
sharing to improve sample complexity. Our method assigns clinician-validated
labels to population-scale biomedical video repositories, helping outperform
traditional supervision by 36.8 F1 points and addressing a key use case where
machine learning has been severely limited by the lack of expert labeled data.
On average, Dugong improves over traditional supervision by 16.0 F1 points and
existing weak supervision approaches by 24.2 F1 points across several video and
sensor classification tasks.
