Weak Supervision with Snorkel

An image classification study built on weak supervision rather than manually labelled data, using Snorkel on the Open Images dataset.

Instead of hand-annotating examples, the project defines labelling functions that encode noisy heuristics, then combines their conflicting votes into probabilistic training labels which are used to train a downstream PyTorch classifier.

The approach is relevant wherever ground-truth annotation is expensive or requires expert time, which is common in biomedical and scientific imaging settings.