What does this research mean for the field?

Deep Transferable Label Propagation (DTLP) integrated with prototypical augmentation outperforms state-of-the-art label propagation-based domain adaptation methods by addressing semantic insufficiency, pseudo-label unreliability, and computational scalability. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To improve the effectiveness of domain adaptation through a novel Deep Transferable Label Propagation (DTLP) strategy that integrates prototypical augmentation.

May 20, 2026Open Access

Deep transferable label propagation with prototypical augmentation

YDYufang DanNingbo University of Technology LZLi ZhuGuangzhou Maritime College DZDi ZhouXidian University

Key Points

To improve the effectiveness of domain adaptation through a novel Deep Transferable Label Propagation (DTLP) strategy that integrates prototypical augmentation.
Developed a prototype-guided feature augmentation module to enhance the source domain's semantic richness.
Constructed a prototype graph for label propagation to reduce computational complexity and address class imbalance.
Utilized prototypical contrastive learning for dynamic optimization of feature extraction and label propagation.
DTLP significantly outperformed existing label propagation methods in various benchmark datasets.
The prototype graph approach reduced computational costs while maintaining label propagation efficiency.
Prototypical augmentation improved model performance, particularly for minority classes.

Abstract

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.

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