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.

翻译：无监督元学习旨在从未标注数据构建的任务分布中学习可泛化的知识。其核心挑战在于如何在缺乏标签信息的情况下构建多样化的元学习任务；近期工作提出了通过预训练表示进行伪标签标注或利用生成模型创建合成样本等方法。然而，这种任务构建策略存在根本性局限：过度依赖元学习过程中不可变的伪标签，以及表示或生成样本的质量。为克服这些局限，我们提出了一种简单高效的无监督元学习框架——伪监督对比学习（Pseudo-supervised Contrast, PsCo），用于少样本分类。受近期自监督学习研究的启发，PsCo利用动量网络和历史批次队列逐步改进伪标签标注并构建多样化任务。大量实验表明，在各类域内和跨域少样本分类基准测试中，PsCo均优于现有无监督元学习方法。我们还验证了PsCo易于扩展至大规模基准测试，而近期前沿元学习方案则不具备这一特性。