Copertina

Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Gecheng, Yang, Zeyu, Luo, Chengwen, Li, Jianqiang
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Co-training partial domain adaptation networks for industrial Fault Diagnosis
di: Chen, Gecheng
Pubblicazione: (2024)

Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis
di: Wu, Wenyi, et al.
Pubblicazione: (2025)

A domain adaptation neural network for digital twin-supported fault diagnosis
di: Chen, Zhenling, et al.
Pubblicazione: (2025)

Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
di: Mohammadi, Hadi, et al.
Pubblicazione: (2025)

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
di: Sha, Yu, et al.
Pubblicazione: (2025)

Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry
di: Gan, Mengjie, et al.
Pubblicazione: (2024)

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases
di: Weng, Futian, et al.
Pubblicazione: (2022)

Causally Perturbed Fairness Testing
di: Du, Chengwen, et al.
Pubblicazione: (2025)

Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions
di: Xia, Pengcheng, et al.
Pubblicazione: (2025)

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
di: Liu, Zhining, et al.
Pubblicazione: (2025)

Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification
di: Rosu, Radu-Andrei, et al.
Pubblicazione: (2024)

Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network
di: Chen, Jiangwen, et al.
Pubblicazione: (2024)

Rethinking Explainable Disease Prediction: Synergizing Accuracy and Reliability via Reflective Cognitive Architecture
di: Shao, Zijian, et al.
Pubblicazione: (2025)

Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
di: Vashishtha, Govind, et al.
Pubblicazione: (2025)

A binary PSO based ensemble under-sampling model for rebalancing imbalanced training data
di: Li, Jinyan, et al.
Pubblicazione: (2025)

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
di: Liu, Jiayue, et al.
Pubblicazione: (2025)

Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
di: Vitale, Francesco, et al.
Pubblicazione: (2025)

Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy
di: Yang, Ruijie, et al.
Pubblicazione: (2026)

TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain
di: Barboule, Camille, et al.
Pubblicazione: (2024)

Two stages domain invariant representation learners solve the large co-variate shift in unsupervised domain adaptation with two dimensional data domains
di: Oshima, Hisashi, et al.
Pubblicazione: (2024)

TMT: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation
di: Zhang, Enming, et al.
Pubblicazione: (2025)

Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm
di: Fang, Jiayan, et al.
Pubblicazione: (2024)

Bi-directional digital twin prototype anchoring with multi-periodicity learning for few-shot fault diagnosis
di: Xia, Pengcheng, et al.
Pubblicazione: (2026)

Graceful task adaptation with a bi-hemispheric RL agent
di: Nicholas, Grant, et al.
Pubblicazione: (2024)

Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
di: Fu, Qingxu, et al.
Pubblicazione: (2024)

Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
di: Mandyam, Aishwarya, et al.
Pubblicazione: (2021)

VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
di: Wu, Hongfei, et al.
Pubblicazione: (2026)

PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management
di: Feng, Xingyu, et al.
Pubblicazione: (2026)

An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation
di: Abboush, Mohammad, et al.
Pubblicazione: (2026)

Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation
di: Gomariz, Alvaro, et al.
Pubblicazione: (2024)

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
di: Gan, Jinling, et al.
Pubblicazione: (2025)

Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution
di: Alomari, Alaa, et al.
Pubblicazione: (2024)

Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
di: Lu, Siyi, et al.
Pubblicazione: (2024)

CorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors
di: Li, Dachong, et al.
Pubblicazione: (2026)

Automated Loss function Search for Class-imbalanced Node Classification
di: Guo, Xinyu, et al.
Pubblicazione: (2024)

Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis
di: Li, Guangqiang, et al.
Pubblicazione: (2024)

FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes
di: Wang, Zexiao, et al.
Pubblicazione: (2025)

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
di: Tan, Haoran, et al.
Pubblicazione: (2026)

Unleashing the True Potential of LLMs: A Feedback-Triggered Self-Correction with Long-Term Multipath Decoding
di: Li, Jipeng, et al.
Pubblicazione: (2025)

TF-MCL: Time-frequency Fusion and Multi-domain Cross-Loss for Self-supervised Depression Detection
di: Zhao, Li-Xuan, et al.
Pubblicazione: (2025)