  The goal of this paper is to design image classification systems that, after
an initial multi-task training phase, can automatically adapt to new tasks
encountered at test time. We introduce a conditional neural process based
approach to the multi-task classification setting for this purpose, and
establish connections to the meta-learning and few-shot learning literature.
The resulting approach, called CNAPs, comprises a classifier whose parameters
are modulated by an adaptation network that takes the current task's dataset as
input. We demonstrate that CNAPs achieves state-of-the-art results on the
challenging Meta-Dataset benchmark indicating high-quality transfer-learning.
We show that the approach is robust, avoiding both over-fitting in low-shot
regimes and under-fitting in high-shot regimes. Timing experiments reveal that
CNAPs is computationally efficient at test-time as it does not involve gradient
based adaptation. Finally, we show that trained models are immediately
deployable to continual learning and active learning where they can outperform
existing approaches that do not leverage transfer learning.
