UvA-DARE (Digital Academic Repository) MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks

This work proposes a variational MetaModulation where the modulation parameters are treated as latent variables and introduces learning variational feature hierarchies by the variational MetaModulation, which modulates features at all layers and can consider task uncertainty and generate more diverse tasks.