Domain shift, characterized by degraded model performance during transition from labeled source domains to unlabeled target domains, poses a persistent challenge for deploying deep learning systems. Current unsupervised domain adaptation (UDA) methods predominantly rely on fine-tuning feature extractors - an approach limited by inefficiency, reduced interpretability, and poor scalability to modern architectures. Our analysis reveals that models pretrained on large-scale data exhibit domain-invariant geometric patterns in their feature space, characterized by intra-class clustering and inter-class separation, thereby preserving transferable discriminative structures. These findings indicate that domain shifts primarily manifest as boundary misalignment rather than feature degradation. Unlike fine-tuning entire pre-trained models - which risks introducing unpredictable feature distortions - we propose the Feature-space Planes Searcher (FPS): a novel domain adaptation framework that optimizes decision boundaries by leveraging these geometric patterns while keeping the feature encoder frozen. This streamlined approach enables interpretative analysis of adaptation while substantially reducing memory and computational costs through offline feature extraction, permitting full-dataset optimization in a single computation cycle. Evaluations on public benchmarks demonstrate that FPS achieves competitive or superior performance to state-of-the-art methods. FPS scales efficiently with multimodal large models and shows versatility across diverse domains including protein structure prediction, remote sensing classification, and earthquake detection. We anticipate FPS will provide a simple, effective, and generalizable paradigm for transfer learning, particularly in domain adaptation tasks. .

翻译：域偏移是指模型在从有标注源域迁移到无标注目标域时性能下降的现象，这对深度学习系统的部署构成了持续挑战。现有无监督域自适应方法主要依赖对特征提取器的微调，这种方案存在效率低下、可解释性降低及对现代架构扩展性不足等问题。我们的分析表明，在大规模数据上预训练的模型，其特征空间中存在域不变几何模式——即类内聚类与类间分离特性，从而保留了可迁移的判别性结构。这些发现表明域偏移主要表现为决策边界错位而非特征退化。与微调整个预训练模型（可能引入不可预测的特征畸变）不同，我们提出特征空间平面搜索器：一种新型域自适应框架，通过冻结特征编码器并利用这些几何模式优化决策边界。这种精简方案既能实现自适应过程的可解释性分析，又通过离线特征提取大幅降低存储与计算成本，支持在单个计算周期内完成全数据集优化。在公开基准上的评估表明，FPS取得了与最优方法相当或更优的性能。该框架能高效适配多模态大模型，并在蛋白质结构预测、遥感分类及地震检测等多元领域展现出通用性。我们预期FPS将为迁移学习（尤其是域自适应任务）提供一种简洁、高效且可推广的技术范式。