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