F EW -S HOT L EARNING WITH W EAK S UPERVISION

This paper proposes a Bayesian gradient-based meta-learning algorithm that can incorporate weak labels to reduce task ambiguity and improve performance and achieves significant performance gains in settings where weak labels are available.