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.

本研究提出了一种名为 “Pseudo-supervised Contrast (PsCo)” 的无监督元学习框架，可以在少样本分类任务中改进伪标签并以渐进方式构建多样化的任务，实验证明该框架在各种领域和跨领域的少样本分类基准测试中表现优异。