FluxLoRA: Flux-Limited Dynamical Low-Rank Adaptation under Noisy Pseudo-Labels

Vision foundation models (VFMs) provide strong transferable representations for semi-supervised learning (SSL), especially when labeled data are limited. However, fine-tuning VFMs with unlabeled data remains sensitive to pseudo-label quality. Incorrect pseudo-labels can introduce noisy supervision and reduce the benefits of VFM pre-training. To address this problem, we propose FluxLoRA, a VFM fine-tuning method designed specifically for SSL under noisy pseudo-label supervision. FluxLoRA enables more stable and noise-robust adaptation while preserving useful information from unlabeled data. Extensive experiments across diverse SSL benchmarks, label budgets, and frozen VFM backbones show that FluxLoRA consistently improves the performance and robustness of VFM-based SSL.