The ability to generalize quickly from few observations is crucial for
intelligent systems. In this paper we introduce APL, an algorithm that
approximates probability distributions by remembering the most surprising
observations it has encountered. These past observations are recalled from an
external memory module and processed by a decoder network that can combine
information from different memory slots to generalize beyond direct recall. We
show this algorithm can perform as well as state of the art baselines on
few-shot classification benchmarks with a smaller memory footprint. In
addition, its memory compression allows it to scale to thousands of unknown
labels. Finally, we introduce a meta-learning reasoning task which is more
challenging than direct classification. In this setting, APL is able to
generalize with fewer than one example per class via deductive reasoning.

本文介绍了一种名为 APL 的算法，其通过记录最令人惊讶的观测结果以近似概率分布，通过解码器网络处理外部存储器模块中的过去观测结果，从而实现快速泛化。该算法在少样本分类基准测试中表现与其他最优算法相当，且内存占用更小，同时其内存压缩功能可扩展到数千个未知标签。此外，本文还介绍了一项元学习推理任务，APL 通过演绎推理可在每个类别少于一个示例的情况下实现推理泛化。