  When data is unlabelled and the target task is not known a priori, divergent
search offers a strategy for learning a wide range of skills. Having such a
repertoire allows a system to adapt to new, unforeseen tasks. Unlabelled image
data is plentiful, but it is not always known which features will be required
for downstream tasks. We propose a method for divergent search in the few-shot
image classification setting and evaluate with Omniglot and Mini-ImageNet. This
high-dimensional behavior space includes all possible ways of partitioning the
data. To manage divergent search in this space, we rely on a meta-learning
framework to integrate useful features from diverse tasks into a single model.
The final layer of this model is used as an index into the `archive' of all
past behaviors. We search for regions in the behavior space that the current
archive cannot reach. As expected, divergent search is outperformed by models
with a strong bias toward the evaluation tasks. But it is able to match and
sometimes exceed the performance of models that have a weak bias toward the
target task or none at all. This demonstrates that divergent search is a viable
approach, even in high-dimensional behavior spaces.
