AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An attention-guided joint distribution domain adaptation method (DRCDA) is proposed that incorporates pseudo-labels from the target domain alongside true labels from the source domain, facilitating compact clustering of similar features while ensuring separation between different classes.
Extensive experiments on multiple bearing fault datasets demonstrate that CSR-DGAN outperforms existing generative augmentation methods in terms of distribution similarity, cross-domain consistency, and downstream diagnostic performance, highlighting the effectiveness of the proposed problem-driven dual-domain generati...
This work proposes DACL-IDA (Dynamic alignment and compactness learning for imbalanced domain adaptation), built on an alignment-scheduling principle rather than a new alignment loss: global adversarial alignment is delayed until classification warmup and black-box shift estimation (BBSE) have stabilized, and is kept r...
In recent years, unsupervised transfer learning based on deep domain adaptation techniques has successfully solved the domain shift problem in machinery fault diagnosis, with a basic assumption that target and source domains share the same label space. However, the label space of the target domain is usually a subset o...
A novel source-free DA framework for rotating machinery fault diagnosis that gradually builds a reliable pseudo-label memory bank and dynamically updates it with historical information is proposed, and the Progressive Pseudo-Labeling strategy is introduced.
Results support the effectiveness of the proposed dual-branch self-guided network (DBSGN) in the studied single-source domain-generalization setting.
A Dynamic Pseudo-Label Guided Adversarial Multi-Scale Graph Convolutional Network (DPAMGCN) for unsupervised cross-domain fault diagnosis and a dynamic threshold-based pseudo-label filtering strategy is proposed that enhances the model's generalization capability.
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