Contrastive Meta-Learning for Partially Observable Few-Shot Learning

Understand

Many contrastive and meta-learning approaches learn representations by identifying common features in multiple views.

• However, the formalism for these approaches generally assumes features to be shared across views to be captured coherently.
• We consider the problem of learning a unified representation from partial observations, where useful features may be present in only some of the views.
• We approach this through a probabilistic formalism enabling views to map to representations with different levels of uncertainty in different components; these views can then be integrated with one another through marginalisation over that uncertainty.