Meta-learning for few-shot learning allows a machine to leverage previously
acquired knowledge as a prior, thus improving the performance on novel tasks
with only small amounts of data. However, most mainstream models suffer from
catastrophic forgetting and insufficient robustness issues, thereby failing to
fully retain or exploit long-term knowledge while being prone to cause severe
error accumulation. In this paper, we propose a novel Continual Meta-Learning
approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically
formulates meta-learning as continual learning of a sequence of tasks. With
each task forming as a graph, the intra- and inter-task correlations can be
well preserved via message-passing and history transition. To remedy
topological uncertainty from graph initialization, we utilize Bayes by Backprop
strategy that approximates the posterior distribution of task-specific
parameters with amortized inference networks, which are seamlessly integrated
into the end-to-end edge learning. Extensive experiments conducted on the
miniImageNet and tieredImageNet datasets demonstrate the effectiveness and
efficiency of the proposed method, improving the performance by 42.8% compared
with state-of-the-art on the miniImageNet 5-way 1-shot classification task.

提出了一种新颖的连续元学习方法，使用贝叶斯图神经网络 (CML-BGNN) 将元学习数学公式化为一系列任务的连续学习，在图形上保留任务内部和任务之间的相关性，利用 Amortized inference networks 解决了图形初始化的拓扑不确定性，提高了 minImageNet  5-way 1-shot 分类任务的分类性能。