基于域划分的渐进式学习用于无监督源域自适应
Domain-Division based Progressive Learning for Source-Free Domain Adaptation
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中文总结 AI 辅助
针对无监督源域自适应中现有方法忽略不可靠样本的问题，提出DPL方法，通过交替划分目标域并定制学习策略，在多个基准上取得优于SOTA的性能。
中文摘要 AI 辅助
随着隐私和可移植性问题日益凸显，无监督源域自适应仅需一个源预训练模型和一个未标记的目标域，即可实现对目标数据的有效自适应。现有大多数自训练方法聚焦于选择和利用预测可靠的样本，却往往忽略了其他样本。受深度模型学习干净样本速度快于噪声样本这一发现的启发，我们提出了一种名为DPL的基于域划分的渐进式学习方法。具体而言，该方法包含两个交替阶段，每个阶段均先根据自适应难度将目标域划分为易自适应和难自适应子域，随后进行基于邻域的伪标签分配。第一阶段，我们通过感知不确定性的自训练以及子域间对应类别的对齐来提升分类准确率；第二阶段则针对每个子域应用定制化学习策略，先对易自适应样本进行一致性学习，再利用局部结构信息处理更具挑战性的样本，从而挖掘目标数据的固有属性。在多个广泛使用的基准上开展的大量实验验证了该方法的有效性，其性能优于当前最先进的方法，代码可在指定URL获取。
英文摘要
With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, allowing for effective adaptation to the target data. Most existing self-training methods focus on selecting and exploiting samples with reliable predictions, often neglecting others. Inspired by the finding that deep models learn clean samples faster than noisy ones, we propose a domain-division based progressive learning method named DPL. Specifically, our approach consists of two alternating stages, each beginning with the division of the target domain into easy-to-adapt and hard-to-adapt subdomains based on adaptation difficulty, followed by neighborhood-based pseudo label assignment. In stage one, we enhance classification accuracy through uncertainty-aware self-training and alignment of corresponding classes between subdomains. Stage two then applies tailored learning strategies to each subdomain, starting with consistency learning on the easy-to-adapt samples and progressing to utilizing local structural information for the more challenging ones, thereby mining the intrinsic properties of the target data. Extensive experiments on several widely used benchmarks validate the effectiveness of our approach, demonstrating superior performance compared to state-of-the-art methods. Our code is available at https://github.com/iamjingli/DPL.
发表机构
- School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）
- College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
机构由 AI 辅助整理，请以论文原文为准。
