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.

本文提出了一种名为 MetaModulation 的方法，在元训练过程中使用神经网络调制批量归一化参数，以提高元训练任务的密度，并通过改变不同层面的参数来增加任务的多样性，同时提出了利用变分 MetaModulation 学习变分特征层级的方法，能够考虑任务不确定性和生成更多样的任务，结果表明 MetaModulation 和其变分变体在四个 few-task meta-learning 基准测试中都优于现有技术。