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.

翻译：元学习算法能够利用先前学到的知识学习新任务，但常需要大量元训练任务，而这在实际中难以获得。为解决该问题，我们提出一种面向较少任务的少样本学习方法——元调制（MetaModulation）。其核心思想是在元训练过程中通过神经网络调制批归一化参数来增加元训练任务的密度。此外，我们在网络不同层级（而非单一层）调整参数以提升任务多样性。针对有限训练任务导致的不确定性，我们进一步提出变分元调制（Variational MetaModulation），将调制参数视为隐变量。通过变分元调制学习变分特征层次，该方法可调制所有层的特征，同时考虑任务不确定性并生成更丰富的任务。消融实验表明，在不同层级使用可学习的任务调制具有优势，并验证了在少任务元学习中引入概率变体的有效性。我们的元调制及其变体在四个少任务元学习基准上持续超越现有最优方法。