用轻量微调让大模型在长尾半监督中更准更稳，尤其能抗住未知数据干扰。
LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios
- 基于大模型微调，仅更新少量参数提升长尾学习效果。
- 在多个数据集上准确率超越现有方法，长尾类别提升显著。
- 适合资源有限但需处理复杂真实场景的模型部署者。
长尾半监督学习（LTSSL）面临尾部样本稀缺与伪标签噪声的双重挑战。现有方法多从头训练，易导致过自信和伪标签质量差。本文首次理论证明：使用基础模型可降低假设复杂度，收紧泛化界，从而最小化平衡后验误差（BPE）；同时，基础模型特征紧凑性严格压缩了异常值接受区域，提供几何鲁棒性保障。受此启发，我们提出新框架LoFT（基于参数高效微调的长尾半监督学习），并进一步拓展至开放世界场景（含分布外样本）。提出LoFT-OW以增强判别能力。在多个基准测试中，所提方法表现优异，代码已开源：https://github.com/games-liker/LoFT。
原文摘要 · Abstract (English)
Long-tailed semi-supervised learning (LTSSL) presents a formidable challenge where models must overcome the scarcity of tail samples while mitigating the noise from unreliable pseudo-labels. Most prior LTSSL methods are designed to train models from scratch, which often leads to issues such as overconfidence and low-quality pseudo-labels. To address this problem, we first theoretically prove that utilizing a foundation model significantly reduces the hypothesis complexity, which tightens the generalization bound and in turn minimizes the Balanced Posterior Error (BPE). Furthermore, we demonstrate that the feature compactness of foundation models strictly compresses the acceptance region for outliers, providing a geometric guarantee for robustness. Motivated by these theoretical insights, we extend LTSSL into the foundation model fine-tuning paradigm and propose a novel framework: LoFT (Long-tailed semi-supervised learning via parameter-efficient Fine-Tuning). Furthermore, we explore a more practical setting by investigating semi-supervised learning under open-world conditions, where the unlabeled data may include out-of-distribution (OOD) samples.To handle this problem, we propose LoFT-OW (LoFT under Open-World scenarios) to improve the discriminative ability. Experimental results on multiple benchmarks demonstrate that our method achieves superior performance. Code is available: https://github.com/games-liker/LoFT
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