Scholarlink

<p>Domain adaptation (DA) seeks to utilize ample labeled data from a source domain to boost the generalization capability of models on an unlabeled target domain with divergent data distributions. Label Propagation (LP) has emerged as an efficient semi-supervised learning paradigm for DA, transferring labels between the source and target domains based on a similarity graph. Nevertheless, existing LP-based DA methods still face significant challenges: (1) Semantic insufficiency in the source training domain impairs the performance of classes with sparse structures, particularly minority classes; (2) Generated pseudo-labels exhibit low reliability due to ambiguous feature distributions; (3) The two-phase architecture decouples domain-invariant feature learning from label propagation, thus failing to achieve mutual enhancement between these two processes for DA tasks; (4) Sample-level graph construction incurs prohibitive computational costs and poor scalability when handling large-scale datasets. To address these issues, we propose a novel DA strategy, Deep Transferable Label Propagation (DTLP), that integrates prototypical augmentation techniques. Specifically, DTLP embeds three core modules into a unified end-to-end system: (1) Prototype-guided feature augmentation, termed Prototypical Augmentation (ProAug), which enriches the semantic content of the source domain by interpolating samples with class prototypes to mitigate semantic deficiency; (2) Prototype graph-based label propagation, which constructs a class-level prototypical graph rather than a sample-level one to reduce computational complexity and alleviate class imbalance; (3) Domain alignment via prototypical contrastive learning, which facilitates dynamic mutual optimization between domain-invariant feature extraction and robust label propagation while narrowing domain discrepancy. Comprehensive experiments on various benchmark datasets demonstrate that the proposed DTLP outperforms state-of-the-art LP-based DA methods, validating its effectiveness and generalizability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep transferable label propagation with prototypical augmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月18日

查看全文
  • Yufang Dan,
  • Li Zhu,
  • Di Zhou

摘要

Domain adaptation (DA) seeks to utilize ample labeled data from a source domain to boost the generalization capability of models on an unlabeled target domain with divergent data distributions. Label Propagation (LP) has emerged as an efficient semi-supervised learning paradigm for DA, transferring labels between the source and target domains based on a similarity graph. Nevertheless, existing LP-based DA methods still face significant challenges: (1) Semantic insufficiency in the source training domain impairs the performance of classes with sparse structures, particularly minority classes; (2) Generated pseudo-labels exhibit low reliability due to ambiguous feature distributions; (3) The two-phase architecture decouples domain-invariant feature learning from label propagation, thus failing to achieve mutual enhancement between these two processes for DA tasks; (4) Sample-level graph construction incurs prohibitive computational costs and poor scalability when handling large-scale datasets. To address these issues, we propose a novel DA strategy, Deep Transferable Label Propagation (DTLP), that integrates prototypical augmentation techniques. Specifically, DTLP embeds three core modules into a unified end-to-end system: (1) Prototype-guided feature augmentation, termed Prototypical Augmentation (ProAug), which enriches the semantic content of the source domain by interpolating samples with class prototypes to mitigate semantic deficiency; (2) Prototype graph-based label propagation, which constructs a class-level prototypical graph rather than a sample-level one to reduce computational complexity and alleviate class imbalance; (3) Domain alignment via prototypical contrastive learning, which facilitates dynamic mutual optimization between domain-invariant feature extraction and robust label propagation while narrowing domain discrepancy. Comprehensive experiments on various benchmark datasets demonstrate that the proposed DTLP outperforms state-of-the-art LP-based DA methods, validating its effectiveness and generalizability.