用预训练模型做半监督学习，发现仅微调有标签数据就可媲美传统方法。
Revisiting semi-supervised learning in the era of foundation models
- 用参数高效微调有标签数据，替代传统半监督学习流程。
- 通过集成多个模型和骨干网络，提升伪标签质量，减少噪声影响。
- 为大模型时代的半监督学习提供简单有效的实用方案。
半监督学习（SSL）利用大量无标签数据与少量有标签数据提升模型性能。随着视觉基础模型（VFMs）成为视觉应用的核心，其与半监督学习的交互机制尚不清晰。为此，我们构建了新的基准数据集，其中冻结的VFMs表现不佳，并系统评估了典型的SSL方法。令人意外的是，仅使用有标签数据进行参数高效微调（PEFT）的表现常常与包含无标签数据的SSL方法相当。这一发现促使我们重新审视自训练：利用监督式PEFT模型为无标签数据生成伪标签以进一步训练。为克服伪标签噪声问题，我们提出集成多个PEFT方法和VFM骨干网络，生成更鲁棒的伪标签。实验结果验证了该方法的有效性，为基于基础模型的半监督学习提供了可操作的洞察，推动了更高效、可扩展的半监督学习发展。
原文摘要 · Abstract (English)
Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning. As vision foundation models (VFMs) increasingly serve as the backbone of vision applications, it remains unclear how SSL interacts with these pre-trained models. To address this gap, we develop new SSL benchmark datasets where frozen VFMs underperform and systematically evaluate representative SSL methods. We make a surprising observation: parameter-efficient fine-tuning (PEFT) using only labeled data often matches SSL performance, even without leveraging unlabeled data. This motivates us to revisit self-training, a conceptually simple SSL baseline, where we use the supervised PEFT model to pseudo-label unlabeled data for further training. To overcome the notorious issue of noisy pseudo-labels, we propose ensembling multiple PEFT approaches and VFM backbones to produce more robust pseudo-labels. Empirical results validate the effectiveness of this simple yet powerful approach, providing actionable insights into SSL with VFMs and paving the way for more scalable and practical semi-supervised learning in the era of foundation models.
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