  Unsupervised meta-learning aims to learn generalizable knowledge across a
distribution of tasks constructed from unlabeled data. Here, the main challenge
is how to construct diverse tasks for meta-learning without label information;
recent works have proposed to create, e.g., pseudo-labeling via pretrained
representations or creating synthetic samples via generative models. However,
such a task construction strategy is fundamentally limited due to heavy
reliance on the immutable pseudo-labels during meta-learning and the quality of
the representations or the generated samples. To overcome the limitations, we
propose a simple yet effective unsupervised meta-learning framework, coined
Pseudo-supervised Contrast (PsCo), for few-shot classification. We are inspired
by the recent self-supervised learning literature; PsCo utilizes a momentum
network and a queue of previous batches to improve pseudo-labeling and
construct diverse tasks in a progressive manner. Our extensive experiments
demonstrate that PsCo outperforms existing unsupervised meta-learning methods
under various in-domain and cross-domain few-shot classification benchmarks. We
also validate that PsCo is easily scalable to a large-scale benchmark, while
recent prior-art meta-schemes are not.
