  This paper presents a meta-learning framework for few-shots One-Class
Classification (OCC) at test-time, a setting where labeled examples are only
available for the positive class, and no supervision is given for the negative
example. We consider that we have a set of `one-class classification'
objective-tasks with only a small set of positive examples available for each
task, and a set of training tasks with full supervision (i.e. highly imbalanced
classification). We propose an approach using order-equivariant networks to
learn a 'meta' binary-classifier. The model will take as input an example to
classify from a given task, as well as the corresponding supervised set of
positive examples for this OCC task. Thus, the output of the model will be
'conditioned' on the available positive example of a given task, allowing to
predict on new tasks and new examples without labeled negative examples. In
this paper, we are motivated by an astronomy application. Our goal is to
identify if stars belong to a specific stellar group (the 'one-class' for a
given task), called \textit{stellar streams}, where each stellar stream is a
different OCC-task. We show that our method transfers well on unseen (test)
synthetic streams, and outperforms the baselines even though it is not
retrained and accesses a much smaller part of the data per task to predict
(only positive supervision). We see however that it doesn't transfer as well on
the real stream GD-1. This could come from intrinsic differences from the
synthetic and real stream, highlighting the need for consistency in the
'nature' of the task for this method. However, light fine-tuning improve
performances and outperform our baselines. Our experiments show encouraging
results to further explore meta-learning methods for OCC tasks.
