arXiv:2604.20928v1 Announce Type: new Abstract: Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault diagnosis (SSDGFD) provides a practical solution by jointly exploiting labeled and unlabeled source domains. However, existing methods still suffer from two coupled limitations. First, pseudo-labels fo
Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis
Junyu Ren, Wensheng Gan, Philip S Yu·arXiv cs.LG··1 min read
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