MetaModulation: Learning Variational Feature Hierarchies for Few-Shot
  Learning with Fewer Tasks
Wenfang Sun Yingjun Du Xiantong Zhen Fan Wang Ling Wang Cees G. M. Snoek
Meta-learning algorithms are able to learn a new task using previously
learned knowledge, but they often require a large number of meta-training tasks
which may not be readily available. To address this issue, we propose a method
for few-shot learning with fewer tasks, which we call MetaModulation. The key
idea is to use a neural network to increase the density of the meta-training
tasks by modulating batch normalization parameters during meta-training.
Additionally, we modify parameters at various network levels, rather than just
a single layer, to increase task diversity. To account for the uncertainty
caused by the limited training tasks, we propose a variational MetaModulation
where the modulation parameters are treated as latent variables. We also
introduce learning variational feature hierarchies by the variational
MetaModulation, which modulates features at all layers and can consider task
uncertainty and generate more diverse tasks. The ablation studies illustrate
the advantages of utilizing a learnable task modulation at different levels and
demonstrate the benefit of incorporating probabilistic variants in few-task
meta-learning. Our MetaModulation and its variational variants consistently
outperform state-of-the-art alternatives on four few-task meta-learning
benchmarks.