Domain adaptation: challenges, methods, datasets, and applications
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …
on another set of data (target domain), which is different but has similar properties as the …
Fourier-based augmentation with applications to domain generalization
When deployed on a new domain different from the training set, deep learning often suffers
from severe performance degradation. To combat domain shift, domain adaptation and …
from severe performance degradation. To combat domain shift, domain adaptation and …
Deep joint semantic adaptation network for multi-source unsupervised domain adaptation
Abstract Multi-source Unsupervised Domain Adaptation (MUDA) transfers knowledge
learned from multiple labeled source domains to an unlabeled target domain by minimizing …
learned from multiple labeled source domains to an unlabeled target domain by minimizing …
Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems
High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …
simulations, making full physical space data assimilation impractical. Latent data …
Unsupervised cross-domain fault diagnosis using feature representation alignment networks for rotating machinery
In this article, the problem of the cross-domain fault diagnosis of rotating machinery is
considered. In a practical setting of this approach, the operating platform of the machine may …
considered. In a practical setting of this approach, the operating platform of the machine may …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Label propagation with contrastive anchors for deep semi-supervised superheat degree identification in aluminum electrolysis process
J Wang, S Xie, Y Xie, X Chen - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurate identification of multimodal Superheat Degree (SD) plays a critical decision-
making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are …
making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are …
Training feedforward neural nets in Hopfield-energy-based configuration: A two-step approach
Abstract We introduce Hopfield-Energy-Based Learning, a general learning framework that
is inspired by energy-based models, to train feedforward neural nets. Our approach includes …
is inspired by energy-based models, to train feedforward neural nets. Our approach includes …
Brain multigraph prediction using topology-aware adversarial graph neural network
Brain graphs (ie, connectomes) constructed from medical scans such as magnetic
resonance imaging (MRI) have become increasingly important tools to characterize the …
resonance imaging (MRI) have become increasingly important tools to characterize the …
Collaborative and adversarial deep transfer auto-encoder for intelligent fault diagnosis
Y Ma, J Yang, L Li - Neurocomputing, 2022 - Elsevier
Deep transfer learning provides an advanced analytical tool for intelligent fault diagnosis to
learn shared fault knowledge in industrial scenarios whereby datasets are collected from …
learn shared fault knowledge in industrial scenarios whereby datasets are collected from …