Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

• URL: http://arxiv.org/abs/2509.13185v1
• Date: Tue, 16 Sep 2025 15:39:03 GMT
• Title: Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy
• Authors: Yunchuan Guan, Yu Liu, Ke Zhou, Zhiqi Shen, Jenq-Neng Hwang, Serge Belongie, Lei Li,
• Abstract summary: We show that meta-learning is more efficient with limited entropy and is more robust to label noise and heterogeneous tasks.<n>We propose MINO, a meta-learning framework designed to enhance unsupervised performance.
• Score: 32.59271703559131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning is a powerful paradigm for tackling few-shot tasks. However, recent studies indicate that models trained with the whole-class training strategy can achieve comparable performance to those trained with meta-learning in few-shot classification tasks. To demonstrate the value of meta-learning, we establish an entropy-limited supervised setting for fair comparisons. Through both theoretical analysis and experimental validation, we establish that meta-learning has a tighter generalization bound compared to whole-class training. We unravel that meta-learning is more efficient with limited entropy and is more robust to label noise and heterogeneous tasks, making it well-suited for unsupervised tasks. Based on these insights, We propose MINO, a meta-learning framework designed to enhance unsupervised performance. MINO utilizes the adaptive clustering algorithm DBSCAN with a dynamic head for unsupervised task construction and a stability-based meta-scaler for robustness against label noise. Extensive experiments confirm its effectiveness in multiple unsupervised few-shot and zero-shot tasks.

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