Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning

• URL: http://arxiv.org/abs/2303.00996v1
• Date: Thu, 2 Mar 2023 06:10:13 GMT
• Title: Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
• Authors: Huiwon Jang, Hankook Lee, Jinwoo Shin
• Abstract summary: We propose a simple yet effective unsupervised meta-learning framework, coined Pseudo-supervised Contrast (PsCo) for few-shot classification. PsCo outperforms existing unsupervised meta-learning methods under various in-domain and cross-domain few-shot classification benchmarks.
• Score: 72.3506897990639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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