Paper ID: 2503.09707 • Published Mar 12, 2025
Revisiting semi-supervised learning in the era of foundation models
Ping Zhang, Zheda Mai, Quang-Huy Nguyen, Wei-Lun Chao
The Ohio State University
TL;DR
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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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