[2509.09926] LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios

Computer Science > Machine Learning arXiv:2509.09926 (cs) [Submitted on 12 Sep 2025 (v1), last revised 6 Apr 2026 (this version, v4)] Title:LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios Authors:Zhiyuan Huang, Jiahao Chen, Bing Su View a PDF of the paper titled LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios, by Zhiyuan Huang and 2 other authors View PDF HTML (experimental) Abstract: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 ...