  Unsupervised meta-learning approaches rely on synthetic meta-tasks that are
created using techniques such as random selection, clustering and/or
augmentation. Unfortunately, clustering and augmentation are domain-dependent,
and thus they require either manual tweaking or expensive learning. In this
work, we describe an approach that generates meta-tasks using generative
models. A critical component is a novel approach of sampling from the latent
space that generates objects grouped into synthetic classes forming the
training and validation data of a meta-task. We find that the proposed
approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM),
outperforms or is competitive with current unsupervised learning baselines on
few-shot classification tasks on the most widely used benchmark datasets. In
addition, the approach promises to be applicable without manual tweaking over a
wider range of domains than previous approaches.
