  The pioneering method for unsupervised meta-learning, CACTUs, is a
clustering-based approach with pseudo-labeling. This approach is model-agnostic
and can be combined with supervised algorithms to learn from unlabeled data.
However, it often suffers from label inconsistency or limited diversity, which
leads to poor performance. In this work, we prove that the core reason for this
is lack of a clustering-friendly property in the embedding space. We address
this by minimizing the inter- to intra-class similarity ratio to provide
clustering-friendly embedding features, and validate our approach through
comprehensive experiments. Note that, despite only utilizing a simple
clustering algorithm (k-means) in our embedding space to obtain the
pseudo-labels, we achieve significant improvement. Moreover, we adopt a
progressive evaluation mechanism to obtain more diverse samples in order to
further alleviate the limited diversity problem. Finally, our approach is also
model-agnostic and can easily be integrated into existing supervised methods.
To demonstrate its generalization ability, we integrate it into two
representative algorithms: MAML and EP. The results on three main few-shot
benchmarks clearly show that the proposed method achieves significant
improvement compared to state-of-the-art models. Notably, our approach also
outperforms the corresponding supervised method in two tasks.
