Weak Supervision: The Art Of Training ML Models From Noisy Data

A deep learning model’s performance gets better as the size of the dataset increases. However, there is a catch; deep learning models have hundreds of millions of parameters. Meaning, the models require a large amount of labelled data. Hand-labelled training sets are expensive and time consuming (from months to years) to create. Some datasets call for domain expertise (eg: medical-related datasets). More often than not, such labelled datasets cannot be even repurposed for new objectives. Given the associated costs and inflexibility of hand-labelling, training sets pose a big hurdle in deploying machine learning models. Enter weak supervision, a branch of machine learning where limited and imprecise sources can be used to label large amounts of training data in a supervised setting.