Vision transformers in domain adaptation and domain generalization: a study of robustness
Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …
different from those used in the training and validation phases. The discrepancy presents a …
Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation
Abstract Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
domain to tackle the learning tasks on the unlabeled target domain. It can be more …
Empowering unsupervised domain adaptation with large-scale pre-trained vision-language models
Abstract Unsupervised Domain Adaptation (UDA) aims to leverage the labeled source
domain to solve the tasks on the unlabeled target domain. Traditional UDA methods face the …
domain to solve the tasks on the unlabeled target domain. Traditional UDA methods face the …
Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation
Abstract Unsupervised Domain Adaptation (UDA) is a machine learning technique that
facilitates knowledge transfer from a labeled source domain to an unlabeled target domain …
facilitates knowledge transfer from a labeled source domain to an unlabeled target domain …
Towards unsupervised domain adaptation via domain-transformer
As a vital problem in pattern analysis and machine intelligence, Unsupervised Domain
Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source …
Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source …
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 …
Multi-source multi-modal domain adaptation
Learning from multiple modalities has recently attracted increasing attention in many tasks.
However, deep learning-based multi-modal learning cannot guarantee good generalization …
However, deep learning-based multi-modal learning cannot guarantee good generalization …
LSFM: Light Style and Feature Matching for Efficient Cross-Domain Palmprint Recognition
The exceptional feature extraction capabilities of deep neural networks (DNNs) have
significantly advanced palmprint recognition. However, DNNs typically require training and …
significantly advanced palmprint recognition. However, DNNs typically require training and …
Mitigating domain shift in online process monitoring for material extrusion additive manufacturing via transfer learning
The additive manufacturing (AM) method has experienced rapid growth in recent decades.
However, its application in end-use products is constrained by printing defects. Therefore …
However, its application in end-use products is constrained by printing defects. Therefore …
DCST: Dual Cross-Supervision for Transformer-based Unsupervised Domain Adaptation
Abstract Unsupervised Domain Adaptation aims to leverage a source domain with ample
labeled data to tackle tasks on an unlabeled target domain. However, this poses a …
labeled data to tackle tasks on an unlabeled target domain. However, this poses a …